Map-Based Localization

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Map-based localization estimates position and orientation by matching camera, LiDAR, radar, radio, or inertial observations to a reference map. Maps range from point clouds and 3D models to floor plans, road networks, satellite imagery, and compact semantic or topological representations; methods use retrieval and feature matching, geometric registration, particle filters, or pose-graph optimization. The field covers both global relocalization and continuous tracking, with benchmarks spanning indoor buildings, roads, forests, and aerial or GPS-denied settings. A central challenge is balancing accurate, reliable localization against compact maps and efficient computation, especially when scenes change, look alike, or provide incomplete observations.

Latest papers

106 papers16 papers in the last four weeks, up 14% on the four weeks before. 0.1% of all new papers.

Mar 12, 2026

Predictive and adaptive maps for long-term visual navigation in changing environments

In this paper, we compare different map management techniques for long-term visual navigation in changing environments. In this scenario, the navigation system needs to continuously update and refine its feature map in order to adapt to the environment appearance change. To achieve reliable long-term navigation, the map management techniques have to (i) select features useful for the current navigation task, (ii) remove features that are obsolete, (iii) and add new features from the current camera view to the map. We propose several map management strategies and evaluate their performance with regard to the robot localisation accuracy in long-term teach-and-repeat navigation. Our experiments, performed over three months, indicate that strategies which model cyclic changes of the environment appearance and predict which features are going to be visible at a particular time and location, outperform strategies which do not explicitly model the temporal evolution of the changes.

Lucie Halodova, Eliska Dvorakova, Filip Majer +4

Artificial Intelligence Center, Czech Technical University · Technical University of Cartagena, Spain · Australian Centre for Robotic Vision, QUT

Visual Navigation Map-Based Localization Embodied Navigation Visual Place Recognition

Mar 10, 2026

VLM-Loc: Localization in Point Cloud Maps via Vision-Language Models

Text-to-point-cloud (T2P) localization aims to infer precise spatial positions within 3D point cloud maps from natural language descriptions, reflecting how humans perceive and communicate spatial layouts through language. However, existing methods largely rely on shallow text-point cloud correspondence without effective spatial reasoning, limiting their accuracy in complex environments. To address this limitation, we propose VLM-Loc, a framework that leverages the spatial reasoning capability of large vision-language models (VLMs) for T2P localization. Specifically, we transform point clouds into bird's-eye-view (BEV) images and scene graphs that jointly encode geometric and semantic context, providing structured inputs for the VLM to learn cross-modal representations bridging linguistic and spatial semantics. On top of these representations, we introduce a partial node assignment mechanism that explicitly associates textual cues with scene graph nodes, enabling interpretable spatial reasoning for accurate localization. To facilitate systematic evaluation across diverse scenes, we present CityLoc, a benchmark built from multi-source point clouds for fine-grained T2P localization. Experiments on CityLoc demonstrate VLM-Loc achieves superior accuracy and robustness compared to state-of-the-art methods. Our code, model, and dataset are available at repository.

Shuhao Kang, Youqi Liao, Peijie Wang +5

VCIP, CS, Nankai University · Wuhan University · CASIA +6

Map-Based Localization 3D VLMs

Mar 10, 2026

Reconstructing Movement from Sparse Samples: Enhanced Spatio-Temporal Matching Strategies for Low-Frequency Data

This paper explores potential improvements to the Spatial-Temporal Matching algorithm for aligning the GPS trajectories to road networks. While this algorithm is effective, it presents some limitations in computational efficiency and the accuracy of the results, especially in dense environments with relatively high sampling intervals. To address this, the paper proposes four modifications to the original algorithm: a dynamic buffer, an adaptive observation probability, a redesigned temporal scoring function, and a behavioral analysis to account for the historical mobility patterns. The enhancements are assessed using real-world data from the urban area of Milan, and through newly defined evaluation metrics to be applied in the absence of ground truth. The results of the experiment show significant improvements in performance efficiency and path quality across various metrics.

Ali Yousefian, Arianna Burzacchi, Simone Vantini

MOX, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy · Modeling and Simulation of Socio-Technical Systems, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Trento, Italy

Map-Based Localization

Mar 6, 2026

CFEAR-Teach-and-Repeat: Fast and Accurate Radar-only Localization

Reliable localization in prior maps is essential for autonomous navigation, particularly under adverse weather, where optical sensors may fail. We present CFEAR-TR, a teach-and-repeat localization pipeline using a single spinning radar, which is designed for easily deployable, lightweight, and robust navigation in adverse conditions. Our method localizes by jointly aligning live scans to both stored scans from the teach mapping pass, and to a sliding window of recent live keyframes. This ensures accurate and robust pose estimation across different seasons and weather phenomena. Radar scans are represented using a sparse set of oriented surface points, computed from Doppler-compensated measurements. The map is stored in a pose graph that is traversed during localization. Experiments on the held-out test sequences from the Boreas dataset show that CFEAR-TR can localize with an accuracy as low as 0.117 m and 0.096°, corresponding to improvements of up to 63% over the previous state of the art, while running efficiently at 29 Hz. These results substantially narrow the gap to lidar-level localization, particularly in heading estimation. We make the C++ implementation of our work available to the community.

Maximilian Hilger, Daniel Adolfsson, Ralf Becker +2

Chair of Perception for Intelligent Systems, Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, Munich, Germany · Robot Navigation and Perception Lab of the AASS Research Center, Örebro University, Örebro, Sweden · Bosch Rexroth, Germany

Robot Localization Map-Based Localization

Mar 5, 2026

VinePT-Map: Pole-Trunk Semantic Mapping for Resilient Autonomous Robotics in Vineyards

Reliable long-term deployment of autonomous robots in agricultural environments remains challenging due to perceptual aliasing, seasonal variability, and the dynamic nature of crop canopies. Vineyards, characterized by repetitive row structures and significant visual changes across phenological stages, represent a pivotal field challenge, limiting the robustness of conventional feature-based localization and mapping approaches. This paper introduces VinePT-Map, a semantic mapping framework that leverages vine trunks and support poles as persistent structural landmarks to enable season-agnostic and resilient robot localization. The proposed method formulates the mapping problem as a factor graph, integrating GPS, IMU, and RGB-D observations through robust geometrical constraints that exploit vineyard structure. An efficient perception pipeline based on instance segmentation and tracking, combined with a clustering filter for outlier rejection and pose refinement, enables accurate landmark detection using low-cost sensors and onboard computation. To validate the pipeline, we present a multi-season dataset for trunk and pole segmentation and tracking. Extensive field experiments conducted across diverse seasons demonstrate the robustness and accuracy of the proposed approach, highlighting its suitability for long-term autonomous operation in agricultural environments.

Giorgio Audrito, Mauro Martini, Alessandro Navone +2

Department of Electronics and Telecommunications, Politecnico di Torino, 10129, Torino, Italy.

Semantic Mapping Agricultural Robotics Map-Based Localization Robot Localization

Mar 4, 2026

SSR: A Generic Framework for Text-Aided Map Compression for Localization

Mapping is crucial in robotics for localization and downstream decision-making. As robots are deployed in ever-broader settings, the maps they rely on continue to increase in size. However, storing these maps indefinitely (cold storage), transferring them across networks, or sending localization queries to cloud-hosted maps imposes prohibitive memory and bandwidth costs. We propose a text-enhanced compression framework that reduces both memory and bandwidth footprints while retaining high-fidelity localization. The key idea is to treat text as an alternative modality: one that can be losslessly compressed with large language models. We propose leveraging lightweight text descriptions combined with very small image feature vectors, which capture "complementary information" as a compact representation for the mapping task. Building on this, our novel technique, Similarity Space Replication (SSR), learns an adaptive image embedding in one shot that captures only the information "complementary" to the text descriptions. We validate our compression framework on multiple downstream localization tasks, including Visual Place Recognition as well as object-centric Monte Carlo localization in both indoor and outdoor settings. SSR achieves 2 times better compression than competing baselines on state-of-the-art datasets, including TokyoVal, Pittsburgh30k, Replica, and KITTI.

Mohammad Omama, Po-han Li, Harsh Goel +6

The University of Texas at Austin · Honda Research Institute

Embedding Compression Map-Based Localization

Mar 4, 2026

TreeLoc++: Robust 6-DoF LiDAR Localization in Forests with a Compact Digital Forest Inventory

Reliable localization is essential for sustainable forest management, as it allows robots to revisit and monitor the status of individual trees over long periods. In modern forestry, this management is structured around Digital Forest Inventories (DFIs), which encode stems using compact geometric attributes rather than raw data. Despite their central role, DFIs have been overlooked in localization research, and most methods still rely on dense gigabyte-sized point clouds that are costly to store and maintain. To improve upon this, we propose TreeLoc++, a global localization framework that operates directly on DFIs as a discriminative representation, eliminating the need to use the raw point clouds. TreeLoc++ reduces false matches in structurally ambiguous forests and improves the reliability of full 6-DoF pose estimation. It augments coarse retrieval with a pairwise distance histogram that encodes local tree-layout context, subsequently refining candidates via DBH-based filtering and yaw-consistent inlier selection to further reduce mismatches. Furthermore, a constrained optimization leveraging tree geometry jointly estimates roll, pitch, and height, enhancing pose stability and enabling accurate localization without reliance on dense 3D point cloud data. Evaluations on diverse forests across four countries show that TreeLoc++ achieves precise localization with centimeter-level accuracy. We further demonstrate robustness to long-term change by localizing data recorded in 2025 against inventories built from 2023 data, spanning a two-year interval. The system represents 15 sessions spanning 7.98 km of trajectories using only 250KB of map data and outperforms both hand-crafted and learning-based baselines that rely on point cloud maps. This demonstrates the scalability of TreeLoc++ for long-term deployment. TreeLoc++ is open-sourced at \bl{https://github.com/minwoo0611/TreeLoc-plusplus}.

Minwoo Jung, Dongjae Lee, Nived Chebrolu +3

Department of Mechanical Engineering, Seoul National University, Seoul, South Korea · Department of Computer Science and Engineering, Indian Institute of Technology Bombay, India · Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, UK

Robotics LiDAR Localization Map-Based Localization Robot Localization

Mar 2, 2026

Uncertainty-Aware Hierarchical Re-Localization in OpenStreetMap via Semantic Alignment

Monocular re-localization enables robots to estimate camera poses from visual observations. However, many existing methods rely on dense maps or large reference image databases, which face scalability limitations and privacy risks. OpenStreetMap (OSM), as a lightweight privacy-preserving map, offers semantic and geometric information with global scalability. Nonetheless, OSM localization remains challenging due to cross-modal discrepancies between natural images and OSM, as well as the high cost of global map-based localization. In this paper, we propose an uncertainty-aware hierarchical search framework with semantic alignment for localization in OSM. First, object-centric DINO-ViT tokens are exploited to reduce the semantic gap between ground-view observations and OSM vectors. Second, global dense matching is decomposed into coarse FFT correlation and uncertainty-controlled local refinement. Extensive experiments demonstrate that our method significantly improves localization accuracy and speed. When trained on a single dataset, the 3

^\circ

orientation recall of our method even outperforms the 5

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recall of state-of-the-art methods.

Yuchen Zou, Xiao Hu, Lihuang Fang +1

School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China · International Digital Economy Academy, Guangdong, Shenzhen 510085, China · Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China

Map-Based Localization

Feb 27, 2026

Altitude-Adaptive Vision-Only Geo-Localization for UAVs in GPS-Denied Environments

Matching downward-looking unmanned aerial vehicle (UAV) images to georeferenced satellite or aerial map tiles supports local earth observation, map-based interpretation, and coarse geo-initialization when direct positioning signals are degraded or unavailable. A major difficulty in this cross-platform matching task is the scale mismatch caused by large variations in UAV altitude. To address this problem, we propose an altitude-adaptive aerial visual place recognition framework. The method first estimates relative altitude from a single downward-looking image by transforming the input into the frequency domain and formulating altitude estimation as a regression-as-classification problem. The estimated altitude is then used to crop the query image to a canonical scale, after which a classification-then-retrieval visual place recognition module performs coarse map localization. To improve retrieval robustness under varying image quality, we further introduce a quality-adaptive margin classifier and refine the final location by weighted coordinate estimation over the top retrieved candidates. Experiments on two synthetic datasets and two real-flight datasets show that the relative altitude estimation module improves downstream retrieval performance under substantial altitude changes. With our visual place recognition module, altitude adaptation improves average R@1 and R@5 by 41.50 and 56.83 percentage points, respectively, compared with using the same retrieval pipeline without altitude normalization, and the main retrieval pipeline runs at 13.3 frames/s on the workstation used for evaluation. These results indicate that relative altitude estimation provides a useful scale prior for cross-altitude UAV-to-map image matching using georeferenced remote sensing imagery.

Xingyu Shao, Mengfan He, Liangzheng Sun +2

Department of Precision Instrument, Tsinghua University, Beijing 100084, China · School of Instrumentation Science and Optoelectronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China · School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China

Cross-View Geo-Localization Map-Based Localization Visual Place Recognition GNSS-Denied Localization

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 9, 2026

Graph-Loc: Robust Graph-Based LiDAR Pose Tracking with Compact Structural Map Priors under Low Observability and Occlusion

Map-based LiDAR pose tracking is essential for long-term autonomous operation, where onboard map priors need be compact for scalable storage and fast retrieval, while online observations are often partial, repetitive, and heavily occluded. We propose Graph-Loc, a graph-based localization framework that tracks the platform pose against compact structural map priors represented as a lightweight point-line graph. Such priors can be constructed from heterogeneous sources commonly available in practice, including polygon outlines vectorized from occupancy/grid maps and CAD/model/floor-plan layouts. For each incoming LiDAR scan, Graph-Loc extracts sparse point and line primitives to form an observation graph, retrieves a pose-conditioned visible subgraph via LiDAR ray simulation, and performs scan-to-map association through unbalanced optimal transport with a local graph-context regularizer. The unbalanced formulation relaxes mass conservation, improving robustness to missing, spurious, and fragmented structures under occlusion. To enhance stability in low-observability segments, we estimate information anisotropy from the refinement normal matrix and defer updates along weakly constrained directions until sufficient constraints reappear. Experiments on public benchmarks, controlled stress tests, and real-world deployments demonstrate accurate and stable tracking with KB-level priors from heterogeneous map sources, including under geometrically degenerate and sustained occlusion and in the presence of gradual scene changes.

Wentao Zhao, Yihe Niu, Zikun Chen +4

School of Automation and Intelligent Sensing, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China · School of Mathematical Sciences Shanghai Jiao Tong University

LiDAR Localization Map-Based Localization Robot Localization

Dec 2, 2025

GeoBridge++: Fact-Guided Geo-Semantic Bridging for Unified Cross-View Geo-Localization

Cross-view geo-localization infers a location by retrieving geo-tagged reference images matching a query image. However, the traditional satellite-centric paradigm limits robustness when high-resolution or up-to-date satellite imagery is unavailable and underexploits complementary cues across views and modalities. To address these challenges, we propose GeoBridge, a novel model that performs bidirectional matching across views and supports language-to-image retrieval. GeoBridge builds on a novel semantic-anchor mechanism that bridges multi-view features through textual descriptions for robust, flexible localization. We further extend GeoBridge to propose GeoBridge++, a fact-guided geo-semantic bridging framework incorporating real-world geographic knowledge to reduce the ambiguity and instability in appearance-dominated supervision. It integrates structured geographic attributes with visual observations to construct factual descriptions and applies targeted guidance based on modality-specific observable content, thereby enhancing geographic discriminability. GeoBridge++ exploits explicit spatial structures encoded by static maps to build a geo-semantic bridge that adaptively aggregates complementary multi-view information and promotes cross-view consistency. In support of this task, we further construct GeoLoc-MM, a million-scale, multi-view, and multi-scale dataset with aligned drone, satellite, street-view, and static-map imagery at six spatial extents per location, enabling systematic evaluation of arbitrary cross-view retrieval, scale robustness, and cross-view generalization. Extensive experiments show that GeoBridge supports robust cross-view and cross-modal geo-localization, while GeoBridge++ achieves consistent improvements across multiple benchmarks. Code and dataset will be released at https://github.com/MiliLab/GeoBridge.

Zixuan Song, Jing Zhang, Di Wang +6

College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China · Zhongguancun Academy, Beijing 100094, China · School of Computer Science, Wuhan University, Wuhan, China +1

Cross-View Geo-Localization Geospatial Representation Learning Cross-Modal Retrieval Map-Based Localization

Nov 21, 2025

Self-Localizing MIMO Beam Mapping with Continuously Evolving Channel Memory

Machine learning has greatly advanced data-driven channel modeling and resource optimization. However, most existing methods require accurately location-labeled datasets, which are costly to collect and maintain in dynamic environments. This paper develops a self-localizing multiple-input multiple-output (MIMO) beam map framework that constructs a hierarchical wireless memory from highly sparse channel state information (CSI) measurements without explicit location labels. To reduce acquisition and processing overhead, we use beamdomain received signal strength (RSS) as compact inputs and theoretically show that they enable asymptotically unbiased spatial signature estimation. A dual-scale extractor captures intrasnapshot angular dependencies and inter-sample correlations for incomplete observations, and a hybrid temporal encoder is designed to consolidate recent CSI into stable short-term context for physical anchor inference. The inferred anchors spatially index a physically structured radio map embedding that stores long-term channel knowledge, which conditions a diffusion decoder for location-consistent full CSI reconstruction. Such a radio map embedding provides a persistent wireless knowledge representation that can be continuously updated and reused without full CSI acquisition. Experiments show that the proposed framework improves physical-anchor recovery accuracy by over 30% under sparse measurements and achieves more than 20% channel-capacity gain in non-line-of-sight (NLOS) beam tracking over Kalman-filter-based methods.

Wangqian Chen, Junting Chen, Shuguang Cui

School of Science and Engineering, and the Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen), The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China

Wireless Communications Wireless Localization Channel Estimation Map-Based Localization

Oct 7, 2025

Terrain-Aided Navigation Without Raycasting: A Sliding Grid Approach for Point Cloud Processing

We investigate the use of a point cloud measurement in terrain-aided navigation. Our goal is to aid an inertial navigation system, by exploring ways to generate a useful measurement innovation error for effective nonlinear state estimation. We compare two such measurement models that involve the scanning of a digital terrain elevation model: a) one that is based on typical ray-casting from a given pose, that returns the predicted point cloud measurement from that pose, and b) another computationally less intensive one that does not require raycasting and we refer to herein as a sliding grid. Besides requiring a pose, it requires the pattern of the point cloud measurement itself and returns a predicted point cloud measurement. We show that the sliding grid achieves comparable accuracy to raycasting, especially at higher altitudes, while being orders of magnitude faster computationally. By eliminating the need for raycasting, the sliding grid removes the dependency on dedicated GPU hardware or high-performance CPUs, making it well-suited for real-time terrain-aided navigation on resource-constrained embedded platforms.

Abdülbaki Şanlan, Fatih Erol, Murad Abu-Khalaf +1

Aerospace Research Center, Istanbul Technical University, Istanbul, Türkiye · Turkish Aerospace, Istanbul, Türkiye

Robot Navigation Map-Based Localization Inertial Navigation

Oct 5, 2025

TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing

Topological mapping offers a compact and robust representation for navigation, but progress in the field is hindered by the lack of standardized evaluation metrics, datasets, and protocols. Existing systems are assessed using different environments and criteria, preventing fair and reproducible comparisons. Moreover, a key challenge - perceptual aliasing - remains under-quantified, despite its strong influence on system performance. We address these gaps by (1) formalizing topological consistency as the fundamental property of topological maps and showing that localization accuracy provides an efficient and interpretable surrogate metric, and (2) proposing the first quantitative measure of dataset ambiguity to enable fair comparisons across environments. To support this protocol, we curate a diverse benchmark dataset with calibrated ambiguity levels, implement and release deep-learned baseline systems, and evaluate them alongside classical methods. Our experiments and analysis yield new insights into the limitations of current approaches under perceptual aliasing. All datasets, baselines, and evaluation tools are fully open-sourced to foster consistent and reproducible research in topological mapping.

Jiaming Wang, Jizhuo Chen, Diwen Liu +1

Benchmark Design Map-Based Localization Topological Mapping

Aug 25, 2025

GSVisLoc: Generalizable Visual Localization for Gaussian Splatting Scene Representations

We introduce GSVisLoc, a visual localization method designed for 3D Gaussian Splatting (3DGS) scene representations. Given a 3DGS model of a scene and a query image, our goal is to estimate the camera's position and orientation. We accomplish this by robustly matching scene features to image features. Scene features are produced by downsampling and encoding the 3D Gaussians while image features are obtained by encoding image patches. Our algorithm proceeds in three steps, starting with coarse matching, then fine matching, and finally by applying pose refinement for an accurate final estimate. Importantly, our method leverages the explicit 3DGS scene representation for visual localization without requiring modifications, retraining, or additional reference images. We evaluate GSVisLoc on both indoor and outdoor scenes, demonstrating competitive localization performance on standard benchmarks while outperforming existing 3DGS-based baselines. Moreover, our approach generalizes effectively to novel scenes without additional training.

Fadi Khatib, Dror Moran, Guy Trostianetsky +3

Weizmann Institute of Science · NVIDIA

Visual Place Recognition Camera Pose Estimation Map-Based Localization Robot Localization