Simultaneous Localization and Mapping

Also known as SLAM

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Overview

SLAM estimates a robot’s changing position and a consistent map at the same time, enabling navigation where GPS or prior maps are unavailable. Systems combine camera, LiDAR, radar, sonar, and inertial measurements using feature or scan matching, sensor-fusion filters, and factor-graph optimization with loop closure to limit accumulated drift. Dense mapping increasingly uses neural fields, semantic scene graphs, and 3D Gaussian splatting, evaluated on indoor RGB-D benchmarks as well as outdoor driving and field-robot datasets. A central challenge is balancing accuracy, dense and useful maps, and real-time resource limits while remaining reliable in dynamic, low-texture, or changing environments.

Latest papers

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198 papers20 papers in the last four weeks, down 5% on the four weeks before. 0.1% of all new papers.

Apr 1, 2026

Compact Keyframe-Optimized Multi-Agent Gaussian Splatting SLAM

Efficient multi-agent 3D mapping is essential for robotic teams operating in unknown environments, but dense representations hinder real-time exchange over constrained communication links. In multi-agent Simultaneous Localization and Mapping (SLAM), systems typically rely on a centralized server to merge and optimize the local maps produced by individual agents. However, sharing these large map representations, particularly those generated by recent methods such as Gaussian Splatting, becomes a bottleneck in real-world scenarios with limited bandwidth. We present an improved multi-agent RGB-D Gaussian Splatting SLAM framework that reduces communication load while preserving map fidelity. First, we incorporate a compaction step into our SLAM system to remove redundant 3D Gaussians, without degrading the rendering quality. Second, our approach performs centralized loop closure computation without initial guess, operating in two modes: a pure rendered-depth mode that requires no data beyond the 3D Gaussians, and a camera-depth mode that includes lightweight depth images for improved registration accuracy and additional Gaussian pruning. Evaluation on both synthetic and real-world datasets shows up to 85-95% reduction in transmitted data compared to state-of-the-art approaches in both modes, bringing 3D Gaussian multi-agent SLAM closer to practical deployment in real-world scenarios. Code: https://github.com/lemonci/coko-slam

Monica M. Q. Li, Pierre-Yves Lajoie, Jialiang Liu +1

Department of Computer Engineering, Polytechnique Montreal, Montreal, QC, Canada · Department of Mathematics, University of Wisconsin, Madison, WI 53706, USA

Simultaneous Localization and Mapping3DGS SLAMMulti-Agent Collaboration3DGS Compression
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Mar 31, 2026

Semantic Zone-Based Map Management for Stable AI-Integrated Mobile Robots

Recent advances in large AI models (VLMs and LLMs) and joint use of the 3D dense maps, enable mobile robots to provide more powerful and interactive services grounded in rich spatial context. However, deploying both heavy AI models and dense maps on edge robots is challenging under strict memory budgets. When the memory budget is exceeded, required keyframes may not be loaded in time, which can degrade the stability of position estimation and interfering model performance. We proposes a semantic zone-based map management approach to stabilize dense-map utilization under memory constraints. We associate keyframes with semantic indoor regions (e.g., rooms and corridors) and keyframe management at the semantic zone level prioritizes spatially relevant map content while respecting memory constraints. This reduces keyframe loading and unloading frequency and memory usage. We evaluate the proposed approach in large-scale simulated indoor environments and on an NVIDIA Jetson Orin Nano under concurrent SLAM-VLM execution. With Qwen3.5:0.8b, the proposed method improves throughput by 3.3 tokens/s and reduces latency by 21.7% relative to a geometric map-management strategy. Furthermore, while the geometric strategy suffers from out-of-memory failures and stalled execution under memory pressure, the proposed method eliminates both issues, preserving localization stability and enabling robust VLM operation. These results demonstrate that the proposed approach enables efficient dense map utilization for memory constrained, AI-integrated mobile robots. Code is available at: https://github.com/huichangs/rtabmap/tree/segment

Huichang Yun, Seungho Yoo

Dept. of computer Engineering, Pukyong National University, Busan, Korea

Mobile RoboticsSimultaneous Localization and MappingSemantic MappingRobot Localization
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Mar 31, 2026

M2H-MX: Multi-Task Semantic and Geometric Perception for Real-Time Monocular 3D Scene Graph Construction

Monocular cameras are attractive for robotic perception due to their low cost and ease of deployment, yet achieving reliable real-time spatial understanding from a single image stream remains challenging. While recent multi-task dense prediction models have improved per-pixel depth and semantic estimation, translating these advances into stable monocular mapping systems is still non-trivial. This paper presents M2H-MX, a real-time multi-task perception model for monocular spatial understanding. The model preserves multi-scale feature representations while introducing register-gated global context and controlled cross-task interaction in a lightweight decoder, enabling depth and semantic predictions to reinforce each other under strict latency constraints. Its outputs integrate directly into an unmodified monocular SLAM pipeline through a compact perception-to-mapping interface. We evaluate both dense prediction accuracy and in-the-loop system performance. On NYUDv2, M2H-MX-L achieves state-of-the-art results, improving semantic mIoU by 6.6% and reducing depth RMSE by 9.4% over representative multi-task baselines. When deployed in a real-time monocular mapping system on ScanNet, M2H-MX reduces average trajectory error by 60.7% compared to a strong monocular SLAM baseline while producing cleaner metric-semantic maps. These results demonstrate that modern multi-task dense prediction can be reliably deployed for real-time monocular spatial perception in robotic systems.

U. V. B. L. Udugama, George Vosselman, Francesco Nex

Department of Earth Observation Science, University of Twente, Enschede, 7522 NH, The Netherlands

Robotic PerceptionMulti-Task LearningMonocular Depth Estimation3D SGGSimultaneous Localization and Mapping
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Mar 26, 2026

Massive Parallel Deep Reinforcement Learning for Active SLAM

Recent advances in parallel computing and GPU acceleration have created new opportunities for computation-intensive learning problems such as Active SLAM -- where actions are selected to reduce uncertainty and improve joint mapping and localization. However, existing DRL-based approaches remain constrained by the lack of scalable parallel training. In this work, we address this challenge by proposing a scalable end-to-end DRL framework for Active SLAM that enables massively parallel training. Compared with the state of the art, our method significantly reduces training time, supports continuous action spaces and facilitates the exploration of more realistic scenarios. It is released as an open-source framework to promote reproducibility and community adoption.

Martín Arce Llobera, Julio A. Placed, Mariano De Paula +1

Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Autonoma de Buenos Aires, C1428EGA, Argentina · Instituto Tecnológico de Aragón (ITA) and the University of Zaragoza, María de Luna 3-7, Zaragoza, Spain

RL for RoboticsSimultaneous Localization and Mapping
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Mar 24, 2026

Variable-Resolution Virtual Maps for Autonomous Exploration with Unmanned Surface Vehicles (USVs)

Autonomous exploration by unmanned surface vehicles (USVs) in near-shore waters requires reliable localisation and consistent mapping over extended areas, but this is challenged by GNSS degradation, environment-induced localisation uncertainty, and limited on-board computation. Virtual map-based methods explicitly model localisation and mapping uncertainty by tightly coupling factor-graph SLAM with a map uncertainty criterion. However, their storage and computational costs scale poorly with fixed-resolution workspace discretisations, leading to inefficiency in large near-shore environments. Moreover, overvaluing feature-sparse open-water regions can increase the risk of SLAM failure as a result of imbalance between exploration and exploitation. To address these limitations, we propose a Variable-Resolution Virtual Map (VRVM), a computationally efficient method for representing map uncertainty using bivariate Gaussian virtual landmarks placed in the cells of an adaptive quadtree. The adaptive quadtree enables an area-weighted uncertainty representation that keeps coarse, far-field virtual landmarks deliberately uncertain while allocating higher resolution to information-dense regions, and reduces the sensitivity of the map valuation to local refinements of the tree. An expectation-maximisation (EM) planner is adopted to evaluate pose and map uncertainty along frontiers using the VRVM, balancing exploration and exploitation. We evaluate VRVM against several state-of-the-art exploration algorithms in the VRX Gazebo simulator, using a realistic marina environment across different testing scenarios with an increasing level of exploration difficulty. The results indicate that our method offers safer behaviour and better utilisation of on-board computation in GNSS-degraded near-shore environments.

Ye Li, Yewei Huang, Yongchang Xie +4

Department of Mechanical Engineering, University College London, London, UK · Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA · Department of Computer Science, Dartmouth College, Hanover, NH, USA

Simultaneous Localization and MappingMobile RoboticsRobot NavigationAutonomous Robot ExplorationUnmanned Surface Vehicle
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Mar 23, 2026

MapForest: A Modular Field Robotics System for Forest Mapping and Invasive Species Localization

Monitoring and controlling invasive tree species across large forests, parks, and trail networks is challenging due to limited accessibility, reliance on manual scouting, and degraded under-canopy GNSS. We present MapForest, a modular field robotics system that transforms multi-modal sensor data into GIS-ready invasive-species maps. Our system features: (i) a compact, platform-agnostic sensing payload that can be rapidly mounted on UAV, bicycle, or backpack platforms, and (ii) a software pipeline comprising LiDAR-inertial mapping, image-based invasive-species detection, and georeferenced map generation. To ensure reliable operation in GNSS-intermittent environments, we enhance a LiDAR-inertial mapping backbone with covariance-aware GNSS factors and robust loss kernels. We train an object detector to detect the Tree-of-Heaven (Ailanthus altissima) from onboard RGB imagery and fuse detections with the reconstructed map to produce geospatial outputs suitable for downstream decision making. We collected a dataset spanning six sites across urban environments, parks, trails, and forests to evaluate individual system modules, and report end-to-end results on two sites containing Tree-of-Heaven. The enhanced mapping module achieved a trajectory deviation error of 1.95 m over a 1.2 km forest traversal, and the Tree-of-Heaven detector achieved an F1 score of 0.653. The datasets and associated tooling are released to support reproducible research in forest mapping and invasive-species monitoring.

Sandeep Zachariah, Francisco Yandun, Sachet Korada +1

Robotics Institute, Carnegie Mellon University, Pittsburgh, USA.

Simultaneous Localization and MappingRemote Sensing
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Mar 23, 2026

Memory-Efficient Boundary Map for Large-Scale Occupancy Grid Mapping

Determining the occupancy status of locations in the environment is a fundamental task for safety-critical robotic applications. Traditional occupancy grid mapping methods subdivide the environment into a grid of voxels, each associated with one of three occupancy states: free, occupied, or unknown. These methods explicitly maintain all voxels within the mapped volume and determine the occupancy state of a location by directly querying the corresponding voxel that the location falls within. However, maintaining all grid voxels in high-resolution and large-scale scenarios requires substantial memory resources. In this paper, we introduce a novel representation that only maintains the boundary of the mapped volume. Specifically, we explicitly represent the boundary voxels, such as the occupied voxels and frontier voxels, while free and unknown voxels are automatically represented by volumes within or outside the boundary, respectively. As our representation maintains only a closed surface in two-dimensional (2D) space, instead of the entire volume in three-dimensional (3D) space, it significantly reduces memory consumption. Then, based on this 2D representation, we propose a method to determine the occupancy state of arbitrary locations in the 3D environment. We term this method as boundary map. Besides, we design a novel data structure for maintaining the boundary map, supporting efficient occupancy state queries. Theoretical analyses of the occupancy state query algorithm are also provided. Furthermore, to enable efficient construction and updates of the boundary map from the real-time sensor measurements, we propose a global-local mapping framework and corresponding update algorithms. Finally, we will make our implementation of the boundary map open-source on GitHub to benefit the community:https://github.com/hku-mars/BDM.

Benxu Tang, Yunfan Ren, Yixi Cai +6

Mechatronics and Robotic Systems (MaRS) Laboratory, Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China. · Department of Robotics, Perception, and Learning, KTH Royal Institute of Technology, Stockholm, Sweden.

Occupancy Grid MappingSimultaneous Localization and Mapping
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Mar 20, 2026

TRGS-SLAM: IMU-Aided Gaussian Splatting SLAM for Blurry, Rolling Shutter, and Noisy Thermal Images

Thermal cameras offer several advantages for simultaneous localization and mapping (SLAM) with mobile robots: they provide a passive, low-power solution to operating in darkness, are invariant to rapidly changing or high dynamic range illumination, and can see through fog, dust, and smoke. However, uncooled microbolometer thermal cameras, the only practical option in most robotics applications, suffer from significant motion blur, rolling shutter distortions, and fixed pattern noise. In this paper, we present TRGS-SLAM, a 3D Gaussian Splatting (3DGS) based thermal inertial SLAM system uniquely capable of handling these degradations. To overcome the challenges of thermal data, we introduce a model-aware 3DGS rendering method and several general innovations to 3DGS SLAM, including B-spline trajectory optimization with a two-stage IMU loss, view-diversity-based opacity resetting, and pose drift correction schemes. Our system demonstrates accurate tracking on real-world, fast motion, and high-noise thermal data that causes all other tested SLAM methods to fail. Moreover, through offline refinement of our SLAM results, we demonstrate thermal image restoration competitive with prior work that required ground truth poses.

Spencer Carmichael, Katherine A. Skinner

University of Michigan, Ann Arbor, MI USA

Simultaneous Localization and Mapping3DGS SLAMThermal Imaging
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Mar 20, 2026

HortiMulti: A Multi-Sensor Dataset for Localisation and Mapping in Horticultural Polytunnels

Agricultural robotics is gaining increasing relevance in both research and real-world deployment. As these systems are expected to operate autonomously in more complex tasks, the availability of representative real-world datasets becomes essential. While domains such as urban and forestry robotics benefit from large and established benchmarks, horticultural environments remain comparatively under-explored despite the economic significance of this sector. To address this gap, we present HortiMulti, a multimodal, cross-season dataset collected in commercial strawberry and raspberry polytunnels across an entire growing season, capturing substantial appearance variation, dynamic foliage, specular reflections from plastic covers, severe perceptual aliasing, and GNSS-unreliable conditions, all of which directly degrade existing localisation and perception algorithms. The sensor suite includes two 3D LiDARs, four RGB cameras, an IMU, GNSS, and wheel odometry. Ground truth trajectories are derived from a combination of Total Station surveying, AprilTag fiducial markers, and LiDAR-inertial odometry, spanning dense, sparse, and marker-free coverage to support evaluation under both controlled and realistic conditions. We release time-synchronised raw measurements, calibration files, reference trajectories, and baseline benchmarks for visual, LiDAR, and multi-sensor SLAM, with results confirming that current state-of-the-art methods remain inadequate for reliable polytunnel deployment, establishing HortiMulti as a one-stop resource for developing and testing robotic perception systems in horticulture environments.

Shuoyuan Xu, Zhipeng Zhong, Tiago Barros +4

Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire, UK, LE11 3TU · Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal · Antobot Ltd, Arise Innovation Hub, Chelmsford, Essex, UK, CM1 1SQ

Robot LocalizationRobotic PerceptionSimultaneous Localization and MappingAgricultural Robotics
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Mar 18, 2026

Full Stack Navigation, Mapping, and Planning for the Lunar Autonomy Challenge

We present a modular, full-stack autonomy system for lunar surface navigation and mapping developed for the Lunar Autonomy Challenge. Operating in a GNSS-denied, visually challenging environment, our pipeline integrates semantic segmentation, stereo visual odometry, pose graph SLAM with loop closures, and layered planning and control. We leverage lightweight learning-based perception models for real-time segmentation and feature tracking and use a factor-graph backend to maintain globally consistent localization. High-level waypoint planning is designed to promote mapping coverage while encouraging frequent loop closures, and local motion planning uses arc sampling with geometric obstacle checks for efficient, reactive control. We evaluate our approach in the competition's high-fidelity lunar simulator, demonstrating centimeter-level localization accuracy, high-fidelity map generation, and strong repeatability across random seeds and rock distributions. Our solution achieved first place in the final competition evaluation.

Adam Dai, Asta Wu, Keidai Iiyama +4

Stanford University

Robot NavigationSpace RoboticsSimultaneous Localization and MappingGNSS-Denied Localization
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Mar 18, 2026

Visual SLAM with DEM Anchoring for Lunar Surface Navigation

Future lunar missions will require autonomous rovers capable of traversing tens of kilometers across challenging terrain while maintaining accurate localization and producing globally consistent maps. However, the absence of global positioning systems, extreme illumination, and low-texture regolith make long-range navigation on the Moon particularly difficult, as visual-inertial odometry pipelines accumulate drift over extended traverses. To address this challenge, we present a stereo visual simultaneous localization and mapping (SLAM) system that integrates learned feature detection and matching with global constraints from digital elevation models (DEMs). Our front-end employs learning-based feature extraction and matching to achieve robustness to illumination extremes and repetitive terrain, while the back-end incorporates DEM-derived height and surface-normal factors into a pose graph, providing absolute surface constraints that mitigate long-term drift. We validate our approach using both simulated lunar traverse data generated in Unreal Engine and real Moon/Mars analog data collected from Mt. Etna. Results demonstrate that DEM anchoring consistently reduces absolute trajectory error compared to baseline SLAM methods, lowering drift in long-range navigation even in repetitive or visually aliased terrain.

Adam Dai, Guillem Casadesus Vila, Grace Gao

Stanford University William F. Durand Building 496 Lomita Mall Stanford, CA 94305

Simultaneous Localization and MappingVisual SLAMRobot NavigationMap-Based Localization
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Mar 17, 2026

PA-LVIO: Real-Time LiDAR-Visual-Inertial Odometry and Mapping with Pose-Only Bundle Adjustment

Real-time LiDAR-visual-inertial odometry and mapping is crucial for navigation and planning tasks in intelligent transportation systems. This study presents a pose-only bundle adjustment (PA) LiDAR-visual-inertial odometry (LVIO), named PA-LVIO, to meet the urgent need for real-time navigation and mapping. The proposed PA framework for LiDAR and visual measurements is highly accurate and efficient, and it can derive reliable frame-to-frame constraints within multiple frames. A marginalization-free and frame-to-map (F2M) LiDAR measurement model is integrated into the state estimator to eliminate odometry drifts. Meanwhile, an IMU-centric online spatial-temporal calibration is employed to obtain a pixel-wise LiDAR-camera alignment. With accurate estimated odometry and extrinsics, a high-quality and RGB-rendered point-cloud map can be built. Comprehensive experiments are conducted on both public and private datasets collected by wheeled robot, unmanned aerial vehicle (UAV), and handheld devices with 28 sequences and more than 50 km trajectories. Sufficient results demonstrate that the proposed PA-LVIO yields superior or comparable performance to state-of-the-art LVIO methods, in terms of the odometry accuracy and mapping quality. Besides, PA-LVIO can run in real-time on both the desktop PC and the onboard ARM computer. The codes and datasets are open sourced on GitHub (https://github.com/i2Nav-WHU/PA-LVIO) to benefit the community.

Hailiang Tang, Tisheng Zhang, Liqiang Wang +3

the GNSS Research Center, Wuhan University, Wuhan 430079, China · the Hubei Technology Innovation Center for Spatiotemporal Information and Positioning Navigation, Wuhan 430079, China · the Hubei Luojia Laboratory, Wuhan 430079, China

Adaptive Lidar-Inertial OdometrySimultaneous Localization and MappingRobot LocalizationCamera-LiDAR Fusion
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Mar 16, 2026

Thermal Image Refinement with Depth Estimation using Recurrent Networks for Monocular ORB-SLAM3

Autonomous navigation in GPS-denied and visually degraded environments remains challenging for unmanned aerial vehicles (UAVs). To this end, we investigate the use of a monocular thermal camera as a standalone sensor on a UAV platform for real-time depth estimation and simultaneous localization and mapping (SLAM). To extract depth information from thermal images, we propose a novel pipeline employing a lightweight supervised network with recurrent blocks (RBs) integrated to capture temporal dependencies, enabling more robust predictions. The network combines lightweight convolutional backbones with a thermal refinement network (T-RefNet) to refine raw thermal inputs and enhance feature visibility. The refined thermal images and predicted depth maps are integrated into ORB-SLAM3, enabling thermal-only localization. Unlike previous methods, the network is trained on a custom non-radiometric dataset, obviating the need for high-cost radiometric thermal cameras. Experimental results on datasets and UAV flights demonstrate competitive depth accuracy and robust SLAM performance under low-light conditions. On the radiometric VIVID++ (indoor-dark) dataset, our method achieves an absolute relative error of approximately 0.06, compared to baselines exceeding 0.11. In our non-radiometric indoor set, baseline errors remain above 0.24, whereas our approach remains below 0.10. Thermal-only ORB-SLAM3 maintains a mean trajectory error under 0.4 m.

Hürkan Şahin, Huy Xuan Pham, Van Huyen Dang +2

Automatic Control Group (RAT), Paderborn University, 33098 Paderborn, Germany · Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus C, Denmark, and also with Upteko ApS, Denmark

Depth EstimationMonocular Depth EstimationSimultaneous Localization and MappingThermal ImagingImage Enhancement
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Mar 14, 2026

LoCal-RIO: Radar-Inertial Odometry with Loop-Closure IMU Bias Calibration

Millimeter-wave radar enables robust perception in visually degraded environments, yet radar-inertial estimation remains prone to drift: body-frame velocity measurements do not constrain heading and position, and the gyroscope bias, which governs heading drift, is poorly observable over the short horizons of sliding-window estimators. We propose a hierarchical radar-inertial factor graph that separates estimation into a fixed-lag navigation graph, which fuses IMU preintegration, radar velocities, ZUPT, and ground-plane constraints into smooth, low-latency odometry, and a keyframe mapping graph, which combines this odometry with submap registration and loop closures. Loop closures additionally calibrate the IMU: the part of a loop residual explained by a bias error is estimated through preintegration Jacobians chained over the loop interval and enters the navigation graph as a prior on the bias alone. Since this calibration is irreversible, it uses only loop closures accepted by the mapping graph and a cycle-consistency test. Extensive evaluations demonstrate high accuracy and drift-reduced estimation at real-time speeds.

Ali Alridha Abdulkarim, Mikhail Litvinov, Zein Alabdeen Abdulkarim +1

Intelligent Space Robotics Laboratory, Center for Digital Engineering, Skolkovo Institute of Science and Technology.

Simultaneous Localization and MappingFactor Graph OptimizationInertial OdometryRadar-Inertial OdometryRobot State EstimationInertial Navigation
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Mar 11, 2026

D-SLAMSpoof: An Environment-Agnostic LiDAR Spoofing Attack using Dynamic Point Cloud Injection

In this work, we introduce Dynamic SLAMSpoof (D-SLAMSpoof), a novel attack that compromises LiDAR SLAM even in feature-rich environments. The attack leverages LiDAR spoofing, which injects spurious measurements into LiDAR scans through external laser interference. By designing both spatial injection shapes and temporally coordinated dynamic injection patterns guided by scan-matching principles, D-SLAMSpoof significantly improves attack success rates in real-world, feature-rich environments such as urban areas and indoor spaces, where conventional LiDAR spoofing methods often fail. Furthermore, we propose a practical defense method, ISD-SLAM, that relies solely on inertial dead reckoning signals commonly available in autonomous systems. We demonstrate that ISD-SLAM accurately detects LiDAR spoofing attacks, including D-SLAMSpoof, and effectively mitigates the resulting position drift. Our findings expose inherent vulnerabilities in LiDAR-based SLAM and introduce the first practical defense against LiDAR-based SLAM spoofing using only standard onboard sensors, providing critical insights for improving the security and reliability of autonomous systems.

Rokuto Nagata, Kenji Koide, Kazuma Ikeda +2

Department of Electronics and Electrical Engineering, Keio University · Department of Information Technology and Human Factors, the National Institute of Advanced Industrial Science and Technology

Physical Adversarial AttacksSimultaneous Localization and Mapping
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Mar 11, 2026

MirrorDrift: Actuated Mirror-Based Attacks on LiDAR SLAM

LiDAR SLAM provides high-accuracy localization but is fragile to point-cloud corruption because scan matching assumes geometric consistency. Prior physical attacks on LiDAR SLAM largely rely on LiDAR spoofing via external signal injection, which requires sensor-specific timing knowledge and is increasingly mitigated by modern defense mechanisms such as timing obfuscation and injection rejection. In this work, we show that specular reflection offers an injection-free alternative and demonstrate an attack, MirrorDrift, that uses an actuated planar mirror to cause ghost points in LiDAR scans and systematically bias scan-matching correspondences. MirrorDrift optimizes mirror placement, alignment, and actuation. In simulation, it increases the average pose error (APE) by 6.1x over random placement, degrading three SLAM systems to 2.29-3.31 m mean APE. In real-world experiments on a modern LiDAR with state-of-the-art interference mitigation, it induces localization errors of up to 6.03 m. To the best of our knowledge, this is the first successful SLAM-targeted attack against production-grade secure LiDARs.

Rokuto Nagata, Kenji Koide, Kazuma Ikeda +3

Department of Electronics and Electrical Engineering, Keio University · Department of Information Technology and Human Factors, the National Institute of Advanced Industrial Science and Technology

Physical Adversarial AttacksLiDAR LocalizationSimultaneous Localization and Mapping
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Mar 11, 2026

Edge-Assisted Multi-Robot Visual-Inertial SLAM with Efficient Communication

The integration of cloud computing and edge computing is an effective way to achieve global consistent and real-time multi-robot Simultaneous Localization and Mapping (SLAM). Cloud computing effectively solves the problem of limited computing, communication and storage capacity of terminal equipment. However, limited bandwidth and extremely long communication links between terminal devices and the cloud result in serious performance degradation of multi-robot SLAM systems. To reduce the computational cost of feature tracking and improve the real-time performance of the robot, a lightweight SLAM method of optical flow tracking based on pyramid IMU prediction is proposed. On this basis, a centralized multi-robot SLAM system based on a robot-edge-cloud layered architecture is proposed to realize real-time collaborative SLAM. It avoids the problems of limited on-board computing resources and low execution efficiency of single robot. In this framework, only the feature points and keyframe descriptors are transmitted and lossless encoding and compression are carried out to realize real-time remote information transmission with limited bandwidth resources. This design reduces the actual bandwidth occupied in the process of data transmission, and does not cause the loss of SLAM accuracy caused by data compression. Through experimental verification on the EuRoC dataset, compared with the current most advanced local feature compression method, our method can achieve lower data volume feature transmission, and compared with the current advanced centralized multi-robot SLAM scheme, it can achieve the same or better positioning accuracy under low computational load.

Xin Liu, Shuhuan Wen, Jing Zhao +2

the Department of Key Lab of Industrial Computer Control Engineering of Hebei Province, Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, 066004, China. · Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan, China · the Department of Civil and Environmental Engineering at the University of Alberta, Canada. +1

Simultaneous Localization and MappingVisual-Inertial SLAMEdge Computing
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Mar 10, 2026

VarSplat: Uncertainty-aware 3D Gaussian Splatting for Robust RGB-D SLAM

Simultaneous Localization and Mapping (SLAM) with 3D Gaussian Splatting (3DGS) enables fast, differentiable rendering and high-fidelity reconstruction across diverse real-world scenes. However, existing 3DGS-SLAM approaches handle measurement reliability implicitly, making pose estimation and global alignment susceptible to drift in low-texture regions, transparent surfaces, or areas with complex reflectance properties. To this end, we introduce VarSplat, an uncertainty-aware 3DGS-SLAM system that explicitly learns per-splat appearance variance. By using the law of total variance with alpha compositing, we then render differentiable per-pixel uncertainty map via efficient, single-pass rasterization. This map guides tracking, submap registration, and loop detection toward focusing on reliable regions and contributes to more stable optimization. Experimental results on Replica (synthetic) and TUM-RGBD, ScanNet, and ScanNet++ (real-world) show that VarSplat improves robustness and achieves competitive or superior tracking, mapping, and novel view synthesis rendering compared to existing studies for dense RGB-D SLAM.

Anh Thuan Tran, Jana Kosecka

Department of Computer Science, George Mason University

Simultaneous Localization and Mapping3DGS SLAMVisual SLAM3D Gaussian Splatting
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Mar 9, 2026

FoMo: A Multi-Season Dataset for Robot Navigation in Forêt Montmorency

The Forêt Montmorency (FoMo) dataset is a comprehensive multi-season data collection, recorded over the span of one year in a boreal forest. Featuring a unique combination of on- and off-pavement environments with significant environmental changes, the dataset challenges established odometry and SLAM pipelines. Some highlights of the data include the accumulation of snow exceeding 1 m, significant vegetation growth in front of sensors, and operations at the traction limits of the platform. In total, the FoMo dataset includes over 64 km of six diverse trajectories, repeated during 12 deployments throughout the year. The dataset features data from one rotating and one hybrid solid-state lidar, a Frequency Modulated Continuous Wave (FMCW) radar, full-HD images from a stereo camera and a wide lens monocular camera, as well as data from two IMUs. Ground Truth is calculated by post-processing three GNSS receivers mounted on the Uncrewed Ground Vehicle (UGV) and a static GNSS base station. Additional metadata, such as one measurement per minute from an on-site weather station, camera calibration intrinsics, and vehicle power consumption, is available for all sequences. To highlight the relevance of the dataset, we performed a preliminary evaluation of the robustness of a lidar-inertial, radar-gyro, and a visual-inertial localization and mapping techniques to seasonal changes. We show that seasonal changes have serious effects on the re-localization capabilities of the state-of-the-art methods. The dataset and development kit are available at https://fomo.norlab.ulaval.ca.

Matěj Boxan, Gabriel Jeanson, Alexander Krawciw +5

Norlab, Universit´e Laval, Quebec City, Canada · University of Toronto Robotics Institute, Toronto, Canada

Simultaneous Localization and MappingRobotic Data CollectionRobot NavigationLiDAR OdometryRobot Localization
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Mar 7, 2026

MipSLAM: Alias-Free Gaussian Splatting SLAM

This paper introduces MipSLAM, a frequency-aware 3D Gaussian Splatting (3DGS) SLAM framework capable of high-fidelity anti-aliased novel view synthesis and robust pose estimation under varying camera configurations. Existing 3DGS-based SLAM systems often suffer from aliasing artifacts and trajectory drift due to inadequate filtering and purely spatial optimization. To overcome these limitations, we propose an Elliptical Adaptive Anti-aliasing (EAA) algorithm that approximates Gaussian contributions via geometry-aware numerical integration, avoiding costly analytic computation. Furthermore, we present a Spectral-Aware Pose Graph Optimization (SA-PGO) module that reformulates trajectory estimation in the frequency domain, effectively suppressing high-frequency noise and drift through graph Laplacian analysis. Extensive evaluations on Replica and TUM datasets demonstrate that MipSLAM achieves state-of-the-art rendering quality and localization accuracy across multiple resolutions. Code is available at https://github.com/yzli1998/MipSLAM.

Yingzhao Li, Yan Li, Shixiong Tian +3

State Key Laboratory of Robotics and Systems (HIT), Harbin Institute of Technology. · Yangtze River Delta HlT Robot Technology Research Institute. · Department of Computer Science, National University of Singapore.

3DGS SLAMSimultaneous Localization and Mapping3D Gaussian SplattingNovel View SynthesisPose Graph Optimization
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Mar 5, 2026

Loop Closure via Maximal Cliques in 3D LiDAR-Based SLAM

Reliable loop closure detection remains a critical challenge in 3D LiDAR-based SLAM, especially under sensor noise, environmental ambiguity, and viewpoint variation conditions. RANSAC is often used in the context of loop closures for geometric model fitting in the presence of outliers. However, this approach may fail, leading to map inconsistency. We introduce a novel deterministic algorithm, CliReg, for loop closure validation that replaces RANSAC verification with a maximal clique search over a compatibility graph of feature correspondences. This formulation avoids random sampling and increases robustness in the presence of noise and outliers. We integrated our approach into a real- time pipeline employing binary 3D descriptors and a Hamming distance embedding binary search tree-based matching. We evaluated it on multiple real-world datasets featuring diverse LiDAR sensors. The results demonstrate that our proposed technique consistently achieves a lower pose error and more reliable loop closures than RANSAC, especially in sparse or ambiguous conditions. Additional experiments on 2D projection-based maps confirm its generality across spatial domains, making our approach a robust and efficient alternative for loop closure detection.

Javier Laserna, Saurabh Gupta, Oscar Martinez Mozos +2

The authors are with the ETSIDI, Universidad Polit´ecnica de Madrid, 28012 Madrid, Spain. · The authors are with the Center for Robotics, University of Bonn, Germany. · Cyrill Stachniss is additionally with the Lamarr Institute for Machine Learning and Artificial Intelligence, Germany.

Loop Closure DetectionSimultaneous Localization and Mapping
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Mar 5, 2026

AIM-SLAM: Dense Monocular SLAM via Adaptive and Informative Multi-View Keyframe Prioritization with Foundation Model

Recent advances in geometric foundation models have emerged as a promising alternative for addressing the challenge of dense reconstruction in monocular visual simultaneous localization and mapping (SLAM). Although geometric foundation models enable SLAM to leverage variable input views, the previous methods remain confined to two-view pairs or fixed-length inputs without sufficient deliberation of geometric context for view selection. To tackle this problem, we propose AIM-SLAM, a dense monocular SLAM framework that exploits an adaptive and informative multi-view keyframe prioritization with dense pointmap predictions from visual geometry grounded transformer (VGGT). Specifically, we introduce the selective information- and geometric-aware multi-view adaptation (SIGMA) module, which employs voxel overlap and information gain to retrieve a candidate set of keyframes and adaptively determine its size. Furthermore, we formulate a joint multi-view Sim(3) optimization that enforces consistent alignment across selected views, substantially improving pose estimation accuracy. The effectiveness of AIM-SLAM is demonstrated on real-world datasets, where it achieves state-of-the-art pose estimation performance and accurate dense reconstruction results. Our system supports ROS integration, with code is available at https://aimslam.github.io/.

Jinwoo Jeon, Dong-Uk Seo, Eungchang Mason Lee +1

The School of Electrical Engineering, KAIST (Korea Advanced Institute of Science and Technology), Daejeon, 34141, Republic of Korea · KAIST InnoCORE LLM, KAIST, Daejeon, 34141, Republic of Korea

Simultaneous Localization and MappingVisual SLAMCamera Pose EstimationMulti-View 3D Reconstruction
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Mar 4, 2026

HBRB-BoW: A Retrained Bag-of-Words Vocabulary for ORB-SLAM via Hierarchical BRB-KMeans

In visual simultaneous localization and mapping (SLAM), the quality of the visual vocabulary is fundamental to the system's ability to represent environments and recognize locations. While ORB-SLAM is a widely used framework, its binary vocabulary, trained through the k-majority-based bag-of-words (BoW) approach, suffers from inherent precision loss. The inability of conventional binary clustering to represent subtle feature distributions leads to the degradation of visual words, a problem that is compounded as errors accumulate and propagate through the hierarchical tree structure. To address these structural deficiencies, this paper proposes hierarchical binary-to-real-and-back (HBRB)-BoW, a refined hierarchical binary vocabulary training algorithm. By integrating a global real-valued flow within the hierarchical clustering process, our method preserves high-fidelity descriptor information until the final binarization at the leaf nodes. Experimental results demonstrate that the proposed approach yields a more discriminative and well-structured vocabulary than traditional methods, significantly enhancing the representational integrity of the visual dictionary in complex environments. Furthermore, replacing the default ORB-SLAM vocabulary file with our HBRB-BoW file is expected to improve performance in loop closing and relocalization tasks.

Minjae Lee, Sang-Min Choi, Gun-Woo Kim +1

Department of Computer Science and Engineering, Gyeongsang National University, Republic of Korea

Simultaneous Localization and MappingHierarchical Clustering
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Mar 3, 2026

Radar-based Pose Optimization for HD Map Generation from Noisy Multi-Drive Vehicle Fleet Data

High-definition (HD) maps are important for autonomous driving, but their manual generation and maintenance is very expensive. This motivates the usage of an automated map generation pipeline. Fleet vehicles provide sufficient sensors for map generation, but their measurements are less precise, introducing noise into the mapping pipeline. This work focuses on mitigating the localization noise component through aligning radar measurements in terms of raw radar point clouds of vehicle poses of different drives and performing pose graph optimization to produce a globally optimized solution between all drives present in the dataset. Improved poses are first used to generate a global radar occupancy map, aimed to facilitate precise on-vehicle localization. Through qualitative analysis we show contrast-rich feature clarity, focusing on omnipresent guardrail posts as the main feature type observable in the map. Second, the improved poses can be used as a basis for an existing lane boundary map generation pipeline, majorly improving map output compared to its original pure line detection based optimization approach.

Alexander Blumberg, Jonas Merkert, Christoph Stiller

Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

HD Map ConstructionSimultaneous Localization and MappingPose Graph Optimization
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Mar 3, 2026

The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes

The D4D Dataset provides paired endoscopic video and high-quality structured-light geometry for evaluating 3D reconstruction of deforming abdominal soft tissue in realistic surgical conditions. Data were acquired from six porcine cadaver sessions using a da Vinci Xi stereo endoscope and a Zivid structured-light camera, registered via optical tracking and manually curated iterative alignment methods. Three sequence types - whole deformations, incremental deformations, and moved-camera clips - probe algorithm robustness to non-rigid motion, deformation magnitude, and out-of-view updates. Each clip provides rectified stereo images, per-frame instrument masks, stereo depth, start/end structured-light point clouds, curated camera poses and camera intrinsics. In postprocessing, ICP and semi-automatic registration techniques are used to register data, and instrument masks are created. The dataset enables quantitative geometric evaluation in both visible and occluded regions, alongside photometric view-synthesis baselines. Comprising over 300,000 frames and 369 point clouds across 98 curated recordings, this resource can serve as a comprehensive benchmark for developing and evaluating non-rigid SLAM, 4D reconstruction, and depth estimation methods.

Reuben Docea, Rayan Younis, Yonghao Long +10

Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany · Department of Translational Surgical Oncology, NCT/UCC Dresden, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307 Dresden, Germany · Department for Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany +2

3D Reconstruction4D ReconstructionEndoscopic 3D ReconstructionDeformable Shape ReconstructionSimultaneous Localization and Mapping
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Mar 3, 2026

PathSpace: Rapid continuous map approximation for efficient SLAM using B-Splines in constrained environments

Simultaneous Localization and Mapping (SLAM) plays a crucial role in enabling autonomous vehicles to navigate previously unknown environments. Semantic SLAM mostly extends visual SLAM, leveraging the higher density information available to reason about the environment in a more human-like manner. This allows for better decision making by exploiting prior structural knowledge of the environment, usually in the form of labels. Current semantic SLAM techniques still mostly rely on a dense geometric representation of the environment, limiting their ability to apply constraints based on context. We propose PathSpace, a novel semantic SLAM framework that uses continuous B-splines to represent the environment in a compact manner, while also maintaining and reasoning through the continuous probability density functions required for probabilistic reasoning. This system applies the multiple strengths of B-splines in the context of SLAM to interpolate and fit otherwise discrete sparse environments. We test this framework in the context of autonomous racing, where we exploit pre-specified track characteristics to produce significantly reduced representations at comparable levels of accuracy to traditional landmark based methods and demonstrate its potential in limiting the resources used by a system with minimal accuracy loss.

Aduen Benjumea, Andrew Bradley, Alexander Rast +1

School of Engineering, Computing & Mathematics, Oxford Brookes University, Oxford, UK

Simultaneous Localization and Mapping
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Mar 1, 2026

riMESA: Consensus ADMM for Real-World Collaborative SLAM

Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for multi-robot teams as it enables downstream tasks like planning and navigation. However, existing C-SLAM back-end algorithms that are required to solve this problem struggle to address the practical realities of real-world deployments (i.e. communication limitations, outlier measurements, and online operation). In this paper we propose Robust Incremental Manifold Edge-based Separable ADMM (riMESA) -- a robust, incremental, and distributed C-SLAM back-end that is resilient to outliers, reliable in the face of limited communication, and can compute accurate state estimates for a multi-robot team in real-time. Through the development of riMESA, we, more broadly, make an argument for the use of Consensus Alternating Direction Method of Multipliers as a theoretical foundation for distributed optimization tasks in robotics like C-SLAM due to its flexibility, accuracy, and fast convergence. We conclude this work with an in-depth evaluation of riMESA on a variety of C-SLAM problem scenarios and communication network conditions using both synthetic and real-world C-SLAM data. These experiments demonstrate that riMESA is able to generalize across conditions, produce accurate state estimates, operate in real-time, and outperform the accuracy of prior works by a factor >7x on real-world datasets.

Daniel McGann, Michael Kaess

The authors are with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.

Simultaneous Localization and MappingPose Graph OptimizationAlternating Direction Method of MultipliersDistributed Optimization
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Feb 27, 2026

How IMU Drift Influences Multi-Radar Inertial Odometry for Ground Robots in Subterranean Terrains

Reliable radar inertial odometry (RIO) requires mitigating IMU bias drift, a challenge that intensifies in subterranean environments due to extreme temperatures and gravity-induced accelerations. Cost-effective IMUs such as the Pixhawk, when paired with FMCW TI IWR6843AOP EVM radars, suffer from drift-induced degradation compounded by sparse, noisy, and flickering radar returns, making fusion less stable than LiDAR-based odometry. Yet, LiDAR fails under smoke, dust, and aerosols, whereas FMCW radars remain compact, lightweight, cost-effective, and robust in these situations. To address these challenges, we propose a two-stage MRIO framework that combines an IMU bias estimator for resilient localization and mapping in GPS-denied subterranean environments affected by smoke. Radar-based ego-velocity estimation is formulated through a least-squares approach and incorporated into an EKF for online IMU bias correction; the corrected IMU accelerations are fused with heterogeneous measurements from multiple radars and an IMU to refine odometry. The proposed framework further supports radar-only mapping by exploiting the robot's estimated translational and rotational displacements. In subterranean field trials, MRIO delivers robust localization and mapping, outperforming EKF-RIO. It maintains accuracy across cost-efficient FMCW radar setups and different IMUs, showing resilience with Pixhawk and higher-grade units such as VectorNav. The implementation will be provided as an open-source resource to the community (code available at https://github.com/LTU-RAI/MRIO

Moumita Mukherjee, Magnus Norén, Anton Koval +2

Radar-Inertial OdometryRoboticsInertial NavigationSimultaneous Localization and Mapping
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Feb 25, 2026

Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environments

In addition to the core tasks of simultaneous localization and mapping (SLAM), active SLAM additionally in- volves generating robot actions that enable effective and efficient exploration of unknown environments. However, existing active SLAM pipelines are limited by three main factors. First, they inherit the restrictions of the underlying SLAM modules that they may be using. Second, their motion planning strategies are typically shortsighted and lack long-term vision. Third, most approaches struggle to handle dynamic scenes. To address these limitations, we propose a novel monocular active SLAM method, Dream-SLAM, which is based on dreaming cross-spatio-temporal images and semantically plausible structures of partially observed dynamic environments. The generated cross-spatio-temporal im- ages are fused with real observations to mitigate noise and data incompleteness, leading to more accurate camera pose estimation and a more coherent 3D scene representation. Furthermore, we integrate dreamed and observed scene structures to enable long- horizon planning, producing farsighted trajectories that promote efficient and thorough exploration. Extensive experiments on both public and self-collected datasets demonstrate that Dream-SLAM outperforms state-of-the-art methods in localization accuracy, mapping quality, and exploration efficiency. Source code will be publicly available upon paper acceptance.

Xiangqi Meng, Pengxu Hou, Zhenjun Zhao +4

Thrust of Robotics and Autonomous Systems, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · University of Zaragoza, Zaragoza, Spain. · School of Computation, Information and Technology, Technical University of Munich, Munich, Germany. +1

Simultaneous Localization and MappingVisual SLAMRobot Navigation
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Feb 24, 2026

RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction

Combining 3D Gaussian splatting with Simultaneous Localization and Mapping (SLAM) has gained popularity as it enables continuous 3D environment reconstruction during motion. However, existing methods struggle in dynamic environments, particularly moving objects complicate 3D reconstruction and, in turn, hinder reliable tracking. The emergence of 4D reconstruction, especially 4D Gaussian splatting, offers a promising direction for addressing these challenges, yet its potential for 4D-aware SLAM remains largely underexplored. Along this direction, we propose a robust and efficient framework, namely Reweighting Uncertainty in Gaussian Splatting SLAM (RU4D-SLAM) for 4D scene reconstruction, that introduces temporal factors into spatial 3D representation while incorporating uncertainty-aware perception of scene changes, blurred image synthesis, and dynamic scene reconstruction. We enhance dynamic scene representation by integrating motion blur rendering, and improve uncertainty-aware tracking by extending per-pixel uncertainty modeling, which is originally designed for static scenarios, to handle blurred images. Furthermore, we propose a semantic-guided reweighting mechanism for per-pixel uncertainty estimation in dynamic scenes, and introduce a learnable opacity weight to support adaptive 4D mapping. Extensive experiments on standard benchmarks demonstrate that our method substantially outperforms state-of-the-art approaches in both trajectory accuracy and 4D scene reconstruction, particularly in dynamic environments with moving objects and low-quality inputs. Code available: https://ru4d-slam.github.io

Yangfan Zhao, Hanwei Zhang, Ke Huang +3

Capital Normal University · Saarland University · Xi’an Jiaotong-Liverpool University +1

3DGS SLAMSimultaneous Localization and MappingDynamic Scene ReconstructionUncertainty Quantification
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