Simultaneous Localization and Mapping

Also known as SLAM

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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

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

Feb 21, 2026

IRIS-SLAM: Unified Geo-Instance Representations for Robust Semantic Localization and Mapping

Geometry foundation models have significantly advanced dense geometric SLAM, yet existing systems often lack deep semantic understanding and robust loop closure capabilities. Meanwhile, contemporary semantic mapping approaches are frequently hindered by decoupled architectures and fragile data association. We propose IRIS-SLAM, a novel RGB semantic SLAM system that leverages unified geometric-instance representations derived from an instance-extended foundation model. By extending a geometry foundation model to concurrently predict dense geometry and cross-view consistent instance embeddings, we enable a semantic-synergized association mechanism and instance-guided loop closure detection. Our approach effectively utilizes viewpoint-agnostic semantic anchors to bridge the gap between geometric reconstruction and open-vocabulary mapping. Experimental results demonstrate that IRIS-SLAM significantly outperforms state-of-the-art methods, particularly in map consistency and wide-baseline loop closure reliability.

Tingyang Xiao, Liu Liu, Wei Feng +6

Horizon Robotics · D-Robotics · S-Lab, Nanyang Technological University +1

Open-Vocabulary Semantic Mapping Simultaneous Localization and Mapping Semantic Mapping Loop Closure Detection Geometric Representation Learning

Feb 20, 2026

GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation

Accurate state estimation and multi-modal perception are prerequisites for autonomous legged robots in complex, large-scale environments. To date, no large-scale public legged-robot dataset captures the real-world conditions needed to develop and benchmark algorithms for legged-robot state estimation, perception, and navigation. To address this, we introduce the GrandTour dataset, a multi-modal legged-robotics dataset collected across challenging outdoor and indoor environments, featuring an ANYbotics ANYmal-D quadruped equipped with the Boxi multi-modal sensor payload. GrandTour spans a broad range of environments and operational scenarios across distinct test sites, ranging from alpine scenery and forests to demolished buildings and urban areas, and covers a wide variation in scale, complexity, illumination, and weather conditions. The dataset provides time-synchronized sensor data from spinning LiDARs, multiple RGB cameras with complementary characteristics, proprioceptive sensors, and stereo depth cameras. Moreover, it includes high-precision ground-truth trajectories from satellite-based RTK-GNSS and a Leica Geosystems total station. This dataset supports research in SLAM, high-precision state estimation, and multi-modal learning, enabling rigorous evaluation and development of new approaches to sensor fusion in legged robotic systems. With its extensive scope, GrandTour represents the largest open-access legged-robotics dataset to date. The dataset is available at https://grand-tour.leggedrobotics.com on HuggingFace (ROS-independent), and in ROS formats, along with tools and demo resources.

Turcan Tuna, Jonas Frey, Frank Fu +5

Robotic Systems Lab, ETH Zurich, Zurich 8092, Switzerland · 1ETH Zurich, Switzerland · 2Stanford University, USA +1

Legged Robotics Simultaneous Localization and Mapping Robotic Data Collection Robot State Estimation Robotic Perception

Feb 19, 2026

Topology-Aware Decision Making for Multi-Session Localization and Mapping

Operating in previously visited environments is becoming increasingly crucial for autonomous systems, with direct applications in autonomous driving, surveying, and warehouse or household robotics. This repeated exposure to observing the same areas poses significant challenges for mapping and localization across sessions, particularly in deciding when a prior model is sufficient for reliable localization and when new mapping is required. In this work, we propose a novel multi-session framework that builds on map-based localization, in contrast to the common practice of greedily running full SLAM sessions and trying to find correspondences between the resulting maps. The core contribution is a principled, topology-driven mechanism to detect multi-session mapping needs from the pose-graph structure. Specifically, our approach uses spectral connectivity metrics on the joint pose-graph to identify disconnections and weakly constrained regions, and selectively triggers mapping and loop closing only when the pose-graph topology indicates insufficient structural support. The resulting map and pose-graph are seamlessly integrated into the existing model, reducing accumulated error and enhancing global consistency while avoiding redundant remapping. We validate our method on overlapping sequences from datasets and demonstrate its effectiveness in a real-world mine-like environment.

Lorenzo Montano-Olivan, Julio A. Placed, Luis Montano +1

Instituto Tecnológico de Aragón (ITA), María de Luna 7, Zaragoza, Spain

Simultaneous Localization and Mapping Map-Based Localization

Feb 18, 2026

Markerless Robot Detection and 6D Pose Estimation for Multi-Agent SLAM

The capability of multi-robot SLAM approaches to merge localization history and maps from different observers is often challenged by the difficulty in establishing data association. Loop closure detection between perceptual inputs of different robotic agents is easily compromised in the context of perceptual aliasing, or when perspectives differ significantly. For this reason, direct mutual observation among robots is a powerful way to connect partial SLAM graphs, but often relies on the presence of calibrated arrays of fiducial markers (e.g., AprilTag arrays), which severely limits the range of observations and frequently fails under sharp lighting conditions, e.g., reflections or overexposure. In this work, we propose a novel solution to this problem leveraging recent advances in Deep-Learning-based 6D pose estimation. We feature markerless pose estimation as part of a decentralized multi-robot SLAM system and demonstrate the benefit to the relative localization accuracy among the robotic team. The solution is validated experimentally on data recorded in a test field campaign on a planetary analogous environment.

Markus Rueggeberg, Maximilian Ulmer, Maximilian Durner +4

Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Weßling, Germany · Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Simultaneous Localization and Mapping Relative Pose Estimation

Jan 15, 2026

SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM

Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metric depth to support these tasks. However, despite recent advances in stereo depth estimation on land, computing depth from image pairs in underwater scenes remains challenging. In underwater environments, images are degraded by light attenuation, visual artifacts, and dynamic lighting conditions. Furthermore, real-world underwater scenes frequently lack rich texture useful for stereo depth estimation and 3D reconstruction. As a result, stereo estimation networks trained on in-air data cannot transfer directly to the underwater domain. In addition, there is a lack of real-world underwater stereo datasets for supervised training of neural networks. Poor underwater depth estimation is compounded in stereo-based Simultaneous Localization and Mapping (SLAM) algorithms, making it a fundamental challenge for underwater robot perception. To address these challenges, we propose a novel framework that enables sim-to-real training of underwater stereo disparity estimation networks using simulated data and self-supervised finetuning. We leverage our learned depth predictions to develop SurfSLAM, a novel framework for real-time underwater SLAM that fuses stereo cameras with IMU, barometric, and Doppler Velocity Log (DVL) measurements. Lastly, we collect a challenging real-world dataset of shipwreck surveys using an underwater robot. Our dataset features over 24,000 stereo pairs, along with high-quality, dense photogrammetry models and reference trajectories for evaluation. Through extensive experiments, we demonstrate the advantages of the proposed training approach on real-world data for improving stereo estimation in the underwater domain and for enabling accurate trajectory estimation and 3D reconstruction of complex shipwreck sites.

Onur Bagoren, Seth Isaacson, Sacchin Sundar +6

Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA

Simultaneous Localization and Mapping Metric Depth Estimation Sim-to-Real Transfer Stereo Matching Underwater 3D Reconstruction Underwater Robotics+1

Dec 28, 2025

Rethinking Densification in Gaussian SLAM: A Stationary Sampling Perspective

Gaussian Splatting enables high-fidelity differentiable SLAM by representing scenes as anisotropic Gaussian primitives. Most systems rely on residual-driven densification, which couples refinement with representation growth: accuracy saturates at a moderate primitive count, yet memory grows throughout the trajectory, degrading throughput. We introduce TopoGS-SLAM, a stationary-sampling formulation that decouples refinement from capacity through coverage-triggered Gaussian initialization and bounded-topology optimization, turning mapping into a bounded-dimensional problem with stable scaling. On standard indoor benchmarks, TopoGS-SLAM matches or surpasses densification-based pipelines in localization and reconstruction accuracy while substantially improving memory scalability and throughput, showing that iterative topology mutation is not required for accurate Gaussian SLAM in bounded-area static indoor scenes with sufficient multi-view coverage. Additional details and resources are available at this URL: https://breeze1124.github.io/topogs-slam-project-page/

Wei-Tse Cheng, Yen-Jen Chiou, Yuan-Fu Yang

National Yang Ming Chiao Tung University

Simultaneous Localization and Mapping 3DGS SLAM 3D Gaussian Splatting 3DGS Acceleration Adaptive Gaussian Densification

Dec 10, 2025

Super4DR: 4D Radar-centric Self-supervised Odometry and Gaussian-based Map Optimization

Conventional odometry and mapping methods using visual or LiDAR data often struggle under poor illumination and adverse weather conditions. Although 4D radar is suited for such environments, its sparse and noisy point clouds hinder accurate odometry estimation, while the radar maps suffer from obscure and incomplete structures. Thus, we propose Super4DR, a 4D radar-centric framework for learning-based odometry estimation and gaussian-based map optimization. First, we design a cluster-aware odometry network that incorporates object-level cues from the clustered radar points for inter-frame matching, alongside a hierarchical self-supervision mechanism to overcome outliers through spatio-temporal consistency, knowledge transfer, and feature contrast. Second, we propose using 3D gaussians as an intermediate representation, coupled with a radar-specific growth strategy, selective separation, and multi-view regularization, to recover blurry map areas and those undetected based on image texture. Experiments show that Super4DR achieves a 67% performance gain over prior self-supervised methods, nearly matches supervised odometry, and narrows the map quality disparity with LiDAR while enabling multi-modal image rendering.

Zhiheng Li, Weihua Wang, Qiang Shen +2

Zheng Fang*

Simultaneous Localization and Mapping 3D Gaussian Splatting Robot State Estimation

Dec 10, 2025

Calibrated Keyframe-Segment Selection for Descriptor-Agnostic LiDAR Loop Closure in Repetitive Environments

False LiDAR loop closures can distort pose graphs in repetitive environments, where one query may match several distant places. Genuine revisits, unlike many perceptual aliases, tend to remain consistent across neighboring keyframes. We present Query-Calibrated Segmental Admission (QCSA), a loop-candidate selection method between descriptor retrieval and point-cloud registration. QCSA maps descriptor scores to evidence that distinguishes true loops from hard negatives, accumulates it over short keyframe segments, and admits only the best segment above a threshold fitted to the strongest false segment for each calibration query. The admitted segment forms two local submaps for one Generalized Iterative Closest Point (GICP) registration. QCSA requires pairwise descriptor scores and separate calibration for each descriptor and setting. Across seven descriptors on the SNU Library Dataset (SNULib), QCSA lowers average trajectory error with 53.8% fewer registration attempts than Top-1. With common local submaps and downstream processing on NCLT, it also reduces registration attempts, constraints exceeding the pose-error tolerance, and trajectory error relative to Top-1. HeLiPR Bridge further evaluates repeated outdoor geometry. Our implementation and dataset will be released at: https://github.com/wanderingcar/snu_library_dataset.

Jaehyun Kim, Seungwon Choi, Wonseok Kang +1

Loop Closure Detection Simultaneous Localization and Mapping LiDAR Place Recognition

Nov 28, 2025

Robust 3DGS-based SLAM via Adaptive Kernel Smoothing

In this paper, we challenge the conventional notion in 3DGS-SLAM that rendering quality is the primary determinant of tracking accuracy. We argue that, compared to solely pursuing a perfect scene representation, it is more critical to enhance the robustness of the rasterization process against parameter errors to ensure stable camera pose tracking. To address this challenge, we propose a novel approach that leverages a smooth kernel strategy to enhance the robustness of 3DGS-based SLAM. Unlike conventional methods that focus solely on minimizing rendering error, our core insight is to make the rasterization process more resilient to imperfections in the 3DGS parameters. We hypothesize that by allowing each Gaussian to influence a smoother, wider distribution of pixels during rendering, we can mitigate the detrimental effects of parameter noise from outlier Gaussians. This approach intentionally introduces a controlled blur to the rendered image, which acts as a regularization term, stabilizing the subsequent pose optimization. While a complete redesign of the rasterization pipeline is an ideal solution, we propose a practical and effective alternative that is readily integrated into existing 3DGS frameworks. Our method, termed Corrective Blurry KNN (CB-KNN), adaptively modifies the RGB values and locations of the K-nearest neighboring Gaussians within a local region. This dynamic adjustment generates a smoother local rendering, reducing the impact of erroneous GS parameters on the overall image. Experimental results demonstrate that our approach, while maintaining the overall quality of the scene reconstruction (mapping), significantly improves the robustness and accuracy of camera pose tracking.

Shouhe Zhang, Dayong Ren, Wen Jie Li +4

School of Computer Science and Technology, Xinjiang University · The National Key Laboratory for Novel Software Technology, Nanjing University · Southwest University of Political Science and Law +2

Simultaneous Localization and Mapping 3D Gaussian Splatting 3DGS SLAM Camera Pose Estimation

Nov 3, 2025

TurboMap: GPU-Accelerated Local Mapping for Visual SLAM

In real-time Visual SLAM systems, local mapping must operate under strict latency constraints, as delays degrade map quality and increase the risk of tracking failure. GPU parallelization offers a promising way to reduce latency. However, parallelizing local mapping is challenging due to synchronized shared-state updates and the overhead of transferring large map data structures to the GPU. This paper presents TurboMap, a GPU-parallelized and CPU-optimized local mapping backend that holistically addresses these challenges. We restructure Map Point Creation to enable parallel Keypoint Correspondence Search on the GPU, redesign and parallelize Map Point Fusion, optimize Redundant Keyframe Culling on the CPU, and integrate a fast GPU-based Local Bundle Adjustment solver. To minimize data transfer and synchronization costs, we introduce persistent GPU-resident keyframe storage. Experiments on the EuRoC and TUM-VI datasets show average local mapping speedups of 1.3x and 1.6x, respectively, while preserving accuracy.

Parsa Hosseininejad, Kimia Khabiri, Shishir Gopinath +3

Simon Fraser University. · University at Buffalo.

Simultaneous Localization and Mapping Bundle Adjustment Visual SLAM

Sep 17, 2025

BIM Informed Visual SLAM for Construction Environments

Monitoring building construction sites requires comparing the as-planned design with the as-built state, which can be estimated in real time using Simultaneous Localization and Mapping (SLAM) techniques. However, visual SLAM is prone to trajectory drift in construction environments, producing maps that are geometrically inaccurate with the actual environment. To address this limitation, we augment an existing RGB-D SLAM system with structural priors derived from the Building Information Model (BIM). The system associates detected walls with their BIM counterparts and includes these correspondences as geometric constraints in the back-end optimization, reducing drift and enhancing global consistency. The proposed method operates in real time and is validated on multiple real construction sites, achieving an average trajectory error reduction of 25.23% and a 7.14% improvement in map accuracy over state-of-the-art baselines. Robustness analyses further demonstrate resilience to incomplete BIM data and geometric discrepancies between as-planned models and the as-built environment.

Asier Bikandi-Noya, Miguel Fernandez-Cortizas, Muhammad Shaheer +3

Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, Luxembourg

Simultaneous Localization and Mapping Visual SLAM

Jul 25, 2025

DINO-SLAM: DINO-informed RGB-D SLAM for Neural Implicit and Explicit Representations

This paper presents DINO-SLAM, a DINO-informed design strategy to enhance implicit (Neural Radiance Field -- NeRF) and explicit representations (Gaussian Splatting -- GS) in SLAM systems through the more comprehensive semantics understanding enabled by DINO. This latter alone, however, lacks proper 3D geometry understanding, allowing only for marginal improvements. Therefore, we rely on a Scene Geometry Encoder (SGE) to enrich DINO features into geometry-aware DINO features (geoDINO), to better understand those geometric relationships that vanilla DINO features fail to capture. Building upon it, we propose two foundational paradigms for NeRF and GS SLAM systems integrating geoDINO features. Compared to state-of-the-art methods, our DINO-informed pipelines achieve superior performance on the Replica, ScanNet, and TUM datasets.

Ziren Gong, Xiaohan Li, Fabio Tosi +4

University of Bologna, Italy · Faculty of Dentistry, The University of Hong Kong, China · Rawmantic AI, China +1

Simultaneous Localization and Mapping Neural Representation Geometry 3DGS SLAM Visual SLAM 3D Scene Representation

Apr 14, 2025

Region Based SLAM-Aware Exploration: Efficient and Robust Autonomous Mapping Strategy That Can Scale

Autonomous exploration for mapping unknown large scale environments is a fundamental challenge in robotics, with efficiency in time, stability against map corruption and computational resources being crucial. This paper presents a novel approach to indoor exploration that addresses these key issues in existing methods. We introduce a Simultaneous Localization and Mapping (SLAM)-aware region-based exploration strategy that partitions the environment into discrete regions, allowing the robot to incrementally explore and stabilize each region before moving to the next one. This approach significantly reduces redundant exploration and improves overall efficiency. As the device finishes exploring a region and stabilizes it, we also perform SLAM keyframe marginalization, a technique which reduces problem complexity by eliminating variables, while preserving their essential information. To improves robustness and further enhance efficiency, we develop a checkpoint system that enables the robot to resume exploration from the last stable region in case of failures, eliminating the need for complete re-exploration. Our method, tested in real homes, office and simulations, outperforms state-of-the-art approaches. The improvements demonstrate substantial enhancements in various real world environments, with significant reductions in keyframe usage (85%), submap usage (50% office, 32% home), pose graph optimization time (78-80%), and exploration duration (10-15%). This region-based strategy with keyframe marginalization offers an efficient solution for autonomous robotic mapping.

Megha Maheshwari, Sadegh Rabiee, He Yin +3

Amazon Lab126

Simultaneous Localization and Mapping Mobile Robotics

Oct 7, 2024

2Fast-2Lamaa: Large-Scale Lidar-Inertial Localization and Mapping with Continuous Distance Fields

This paper introduces 2Fast-2Lamaa, a lidar-inertial state estimation framework for odometry, mapping, and localization. Its first key component is the optimization-based undistortion of lidar scans, which uses continuous IMU preintegration to model the system's pose at every lidar point timestamp. The continuous trajectory over 100-200ms is parameterized only by the initial scan conditions (linear velocity and gravity orientation) and IMU biases, yielding eleven state variables. These are estimated by minimizing point-to-line and point-to-plane distances between lidar-extracted features without relying on previous estimates, resulting in a prior-less motion-distortion correction strategy. Because the method performs local state estimation, it directly provides scan-to-scan odometry. To maintain geometric consistency over longer periods, undistorted scans are used for scan-to-map registration. The map representation employs Gaussian Processes to form a continuous distance field, enabling point-to-surface distance queries anywhere in space. Poses of the undistorted scans are refined by minimizing these distances through non-linear least-squares optimization. For odometry and mapping, the map is built incrementally in real time; for pure localization, existing maps are reused. The incremental map construction also includes mechanisms for removing dynamic objects. We benchmark 2Fast-2Lamaa on over 750km of public and self-collected datasets from both automotive and handheld systems. The framework achieves state-of-the-art performance across diverse and challenging scenarios, reaching odometry and localization errors as low as 0.22% and 0.06 m, respectively. The real-time implementation is publicly available at https://github.com/clegenti/2fast2lamaa.

Cedric Le Gentil, Raphael Falque, Daniil Lisus +1

Robotics Institute, University of Toronto Institute for Aerospace Studies, Canada · Robotics Institute, University of Technology Sydney, Australia

Simultaneous Localization and Mapping Inertial Odometry LiDAR Odometry LiDAR Localization

Sep 18, 2024

Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks

Enabling robots to autonomously discover high-level spatial concepts (e.g., rooms and walls) from primitive geometric observations (e.g., planar surfaces) within 3D Scene Graphs is essential for robust indoor navigation and mapping. These graphs provide a hierarchical metric-semantic representation in which such concepts are organized. To further enhance graph-SLAM performance, Factorized 3D Scene Graphs incorporate these concepts as optimization factors that constrain relative geometry and enforce global consistency. However, both stages of this process remain largely manual: concepts are typically derived using hand-crafted, concept-specific heuristics, while factors and their covariances are likewise manually designed. This reliance on manual specification limits generalization across diverse environments and scalability to new concept classes. This paper presents a novel learning-based method that infers spatial concepts online from observed vertical planes and introduces them as optimizable factors within a SLAM backend, eliminating the need to handcraft concept generation, factor design, and covariance specification. We evaluate our approach in simulated environments with complex layouts, improving room detection by 20.7% and trajectory estimation by 19.2%. Validated on real construction sites, room detection improves by 5.3% and map matching accuracy by 3.8%.

Jose Andres Millan-Romera, Muhammad Shaheer, Miguel Fernandez-Cortizas +3

Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg · University of Amsterdam

Simultaneous Localization and Mapping 3D SGG

Aug 26, 2024

A Survey on Reinforcement Learning Applications in SLAM

Simultaneous localization and mapping (SLAM) allows a mobile robot or autonomous vehicle to build a map of an unknown environment while estimating its own pose within that map. Reinforcement learning (RL), in which an agent learns a decision policy from interaction and reward, has been applied to decide how such systems move, explore, and recognize places they have visited before. This survey reviews the applications of RL in SLAM. We first distinguish passive SLAM, in which the robot's motion is not chosen by the SLAM system, from active SLAM, in which it is, and summarize the sensors that provide the input to SLAM. We then introduce the RL methods used in this literature, from value-based and policy-based methods to actor-critic and deep RL. Next, we classify RL applications in SLAM into three categories: path planning, including environment exploration and obstacle avoidance; loop closure detection; and active SLAM. Thirteen representative studies are compared in terms of their simulation environment, deep learning method, SLAM method, and RL algorithm. Most of these studies are evaluated mainly in simulation, and value-based methods from the deep Q-network family are the most common. Finally, we discuss the challenges of applying RL to SLAM, namely computational demands, safety, generalization, high-dimensional state and action spaces, sample efficiency, and sensor and actuator delays, and we outline directions for future research.

Mohammad Dehghani Tezerjani, Mohammad Khoshnazar, Mohammadhamed Tangestanizadeh +2

Computer Science and Engineering, University of North Texas, Denton, USA · Institute of Artificial Intelligence, University of Bremen, Bremen, Germany · Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, USA +1

Simultaneous Localization and Mapping RL for Robotics Robotic RL Loop Closure Detection

May 12, 2024

NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU

Many existing visual SLAM methods can achieve high localization accuracy in dynamic environments by leveraging deep learning to mask moving objects. However, these methods incur significant computational overhead as the camera tracking needs to wait for the deep neural network to generate mask at each frame, and they typically require GPUs for real-time operation, which restricts their practicality in real-world robotic applications. Therefore, this paper proposes a real-time dynamic SLAM system that runs exclusively on a CPU. Our approach incorporates a mask propagation mechanism that decouples camera tracking and deep learning-based masking for each frame. We also introduce a hybrid tracking strategy that integrates ORB features with optical flow methods, enhancing both robustness and efficiency by selectively allocating computational resources to input frames. Compared to previous methods, our system maintains high localization accuracy in dynamic environments while achieving a tracking frame rate of 60 FPS on a laptop CPU. These results demonstrate the feasibility of utilizing deep learning for dynamic SLAM without GPU support. Since most existing dynamic SLAM systems are not open-source, we make our code publicly available at: https://github.com/yuhaozhang7/NGD-SLAM

Yuhao Zhang, Mihai Bujanca, Mikel Luján

University of Manchester. 2University of Cambridge. · 3Qualcomm Technologies XR Labs, Austria.

Simultaneous Localization and Mapping Dynamic SLAM Visual SLAM

Mar 13, 2023

NeuSE: Neural SE(3)-Equivariant Embedding for Long-Term Object-Based Simultaneous Localization and Mapping

We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object-based Simultaneous Localization and Mapping (SLAM) for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial object observations. It serves as a compact point cloud surrogate for complete object models, encoding the full shape, scale, and transform information about an object. In addition, the inferred latent code is both SE(3) and scale equivariant, enabling strong generalization to objects of both unseen sizes and different SE(3) poses. This makes NeuSE particularly effective in real-world scenarios where objects may vary in size or spatial configuration. With NeuSE, relative frame transforms can be directly derived from inferred latent codes. Our proposed SLAM paradigm, using NeuSE for object shape, size, and pose characterization, can operate independently or in conjunction with typical SLAM systems. It directly infers SE(3) camera pose constraints that are compatible with general SLAM pose graph optimization, while maintaining a lightweight, object-centric map that adapts to real-world changes. Our evaluation is conducted on synthetic and real-world sequences with changes in both controlled and uncontrolled settings, featuring multi-category objects of various shapes and sizes. Our approach demonstrates improved localization capability and change-aware mapping consistency when working either independently or as a complement to common SLAM pipelines. Additional results are presented in the supplementary video: https://youtu.be/fZ4_2P1fQBQ

Jiahui Fu, Yilun Du, Kurran Singh +2

MIT CSAIL

Simultaneous Localization and Mapping Visual SLAM Object-Centric Representation Learning Equivariant Representation Learning