We consider the distributed pose-graph optimization (PGO) problem, which is fundamental in accurate trajectory estimation in multi-robot simultaneous localization and mapping (SLAM). Conventional iterative approaches linearize a highly non-convex optimization objective, requiring repeated solving of normal equations, which often converge to local minima and thus produce suboptimal estimates. We propose a scalable, outlier-robust distributed planar PGO framework using Multi-Agent Reinforcement Learning (MARL). We cast distributed PGO as a partially observable Markov game defined on local pose-graphs, where each action refines a single edge's pose estimate. A graph partitioner decomposes the global pose graph, and each robot runs a recurrent edge-conditioned Graph Neural Network (GNN) encoder with adaptive edge-gating to denoise noisy edges. Robots sequentially refine poses through a hybrid policy that utilizes prior action memory and graph embeddings. After local graph correction, a consensus scheme reconciles inter-robot disagreements to produce a globally consistent estimate. Our extensive evaluations on a comprehensive suite of synthetic and real-world datasets demonstrate that our learned MARL-based actors reduce the global objective by an average of 37.5% more than the state-of-the-art distributed PGO framework, while enhancing inference efficiency by at least 6X. We also demonstrate that actor replication allows a single learned policy to scale effortlessly to substantially larger robot teams without any retraining. Code is publicly available at https://github.com/herolab-uga/policies-over-poses.

Figure 1: L T -Exosense is capable of merging multiple sessions generated by a previous work, Exosense, a vision-centric scene understanding system with its sensing unit (T op-Right) integrated into a self-balancing exoskeleton (b). The merged map (a) contains five sessions with colored contours indicating the coverage area of each session. Such a merged map can be further converted into a navigation map, enabling obstacle-free planning spanning multiple sessions. Abstract-- Self-balancing exoskeletons offer a promising mobility solution for individuals with lower-limb disabilities. For reliable long-term operation, these exoskeletons require a perception system that is effective in changing environments. In this work, we introduce L T -Exosense, a vision-centric, multi-session mapping system designed to support long-term (semi)- autonomous navigation for exoskeleton users. L T -Exosense extends single-session mapping capabilities by incrementally fusing spatial knowledge across multiple sessions, detecting environmental changes, and updating a persistent global map. This representation enables intelligent path planning, which can adapt to newly observed obstacles and can recover previous routes when obstructions are removed. We validate L T -Exosense through several real-world experiments, demonstrating a scalable multi-session map that achieves an average point-to-point error below 5 cm when compared to ground-truth laser scans.

-- 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. T o 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.

(Visual) Simultaneous Localization and Mapping (SLAM) remains a fundamental challenge in enabling autonomous systems to navigate and understand large-scale environments. Traditional SLAM approaches struggle to balance efficiency and accuracy, particularly in large-scale settings where extensive computational resources are required for scene reconstruction and Bundle Adjustment (BA). However, this scene reconstruction, in the form of sparse pointclouds of visual landmarks, is often only used within the SLAM system because navigation and planning methods require different map representations. In this work, we therefore investigate a more scalable Visual SLAM (VSLAM) approach without reconstruction, mainly based on approaches for two-view loop closures. By restricting the map to a sparse keyframed pose graph without dense geometry representations, our '2GO' system achieves efficient optimization with competitive absolute trajectory accuracy. In particular, we find that recent advancements in image matching and monocular depth priors enable very accurate trajectory optimization from two-view edges. We conduct extensive experiments on diverse datasets, including large-scale scenarios, and provide a detailed analysis of the trade-offs between runtime, accuracy, and map size. Our results demonstrate that this streamlined approach supports real-time performance, scales well in map size and trajectory duration, and effectively broadens the capabilities of VSLAM for long-duration deployments to large environments.

Mapping and scene representation are fundamental to reliable planning and navigation in mobile robots. While purely geometric maps using voxel grids allow for general navigation, obtaining up-to-date spatial and semantically rich representations that scale to dynamic large-scale environments remains challenging. In this work, we present CURB-OSG, an open-vocabulary dynamic 3D scene graph engine that generates hierarchical decompositions of urban driving scenes via multi-agent collaboration. By fusing the camera and LiDAR observations from multiple perceiving agents with unknown initial poses, our approach generates more accurate maps compared to a single agent while constructing a unified open-vocabulary semantic hierarchy of the scene. Unlike previous methods that rely on ground truth agent poses or are evaluated purely in simulation, CURB-OSG alleviates these constraints. We evaluate the capabilities of CURB-OSG on real-world multi-agent sensor data obtained from multiple sessions of the Oxford Radar RobotCar dataset. We demonstrate improved mapping and object prediction accuracy through multi-agent collaboration as well as evaluate the environment partitioning capabilities of the proposed approach. To foster further research, we release our code and supplementary material at https://ov-curb.cs.uni-freiburg.de.

SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images Y angfan Xu, Qu Hao, Lilian Zhang, Jun Mao, Xiaofeng He, Wenqi Wu, Changhao Chen* Abstract -- Visual SLAM is essential for mobile robots, drone navigation, and VR/AR, but traditional RGB camera systems struggle in low-light conditions, driving interest in thermal SLAM, which excels in such environments. However, thermal imaging faces challenges like low contrast, high noise, and limited large-scale annotated datasets, restricting the use of deep learning in outdoor scenarios. We present DarkSLAM, a noval deep learning-based monocular thermal SLAM system designed for large-scale localization and reconstruction in complex lighting conditions.Our approach incorporates the Efficient Channel Attention (ECA) mechanism in visual odometry and the Selective Kernel Attention (SKA) mechanism in depth estimation to enhance pose accuracy and mitigate thermal depth degradation. Additionally, the system includes thermal depth-based loop closure detection and pose optimization, ensuring robust performance in low-texture thermal scenes. Extensive outdoor experiments demonstrate that DarkSLAM significantly outperforms existing methods like SC-Sfm-Learner and Shin et al., delivering precise localization and 3D dense mapping even in challenging nighttime environments. I. INTRODUCTION Simultaneous Localization and Mapping (SLAM) is crucial for intelligent systems from mobile robots, drones, to self-driving vehicles, enabling their real-time localization and mapping for autonomous navigation. Traditional visual SLAM systems, which rely on visible-light (RGB) cameras, struggle in challenging lighting conditions such as strong light, shadows, or nighttime, limiting their use in all-time scenarios. Thermal cameras, which detect heat radiation, offer a solution by functioning in darkness, smoke, and dust, complementing visible-light sensors. Recent improvements in thermal camera resolution and sensitivity have increased their reliability in autonomous systems.

Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel. However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision drastically degrades in the vicinity of buildings. These inaccuracies hinder aggregation of diverse data from multiple sources in a unified georeferenced frame for SAR operators. In contrast, CityGML models provide approximate building shapes with accurate georeferenced poses. Besides, LiDAR works best in the vicinity of 3D structures. Hence, we refine coarse GNSS measurements by registering LiDAR maps against CityGML and digital elevation map (DEM) models as a prior for allocentric mapping. An intuitive plausibility score selects the best hypothesis based on occupancy using a 2D height map. Afterwards, we integrate the registration results in a continuous-time spline-based pose graph optimizer with LiDAR odometry and further sensing modalities to obtain globally consistent, georeferenced trajectories and maps. We evaluate the viability of our approach on multiple flights captured at two distinct testing sites. Our method successfully reduced GNSS offset errors from up-to 16 m to below 0.5 m on multiple flights. Furthermore, we obtain globally consistent maps w.r.t. prior 3D geospatial models.

A major limitation of minimally invasive surgery is the difficulty in accurately locating the internal anatomical structures of the target organ due to the lack of tactile feedback and transparency. Augmented reality (AR) offers a promising solution to overcome this challenge. Numerous studies have shown that combining learning-based and geometric methods can achieve accurate preoperative and intraoperative data registration. This work proposes a real-time monocular 3D tracking algorithm for post-registration tasks. The ORB-SLAM2 framework is adopted and modified for prior-based 3D tracking. The primitive 3D shape is used for fast initialization of the monocular SLAM. A pseudo-segmentation strategy is employed to separate the target organ from the background for tracking purposes, and the geometric prior of the 3D shape is incorporated as an additional constraint in the pose graph. Experiments from in-vivo and ex-vivo tests demonstrate that the proposed 3D tracking system provides robust 3D tracking and effectively handles typical challenges such as fast motion, out-of-field-of-view scenarios, partial visibility, and "organ-background" relative motion.

Exploration in unknown and unstructured environments is a pivotal requirement for robotic applications. A robot's exploration behavior can be inherently affected by the performance of its Simultaneous Localization and Mapping (SLAM) subsystem, although SLAM and exploration are generally studied separately. In this paper, we formulate exploration as an active mapping problem and extend it with semantic information. We introduce a novel active metric-semantic SLAM approach, leveraging recent research advances in information theory and spectral graph theory: we combine semantic mutual information and the connectivity metrics of the underlying pose graph of the SLAM subsystem. We use the resulting utility function to evaluate different trajectories to select the most favorable strategy during exploration. Exploration and SLAM metrics are analyzed in experiments. Running our algorithm on the Habitat dataset, we show that, while maintaining efficiency close to the state-of-the-art exploration methods, our approach effectively increases the performance of metric-semantic SLAM with a 21% reduction in average map error and a 9% improvement in average semantic classification accuracy.

Large-scale multi-session LiDAR mapping is essential for a wide range of applications, including surveying, autonomous driving, crowdsourced mapping, and multi-agent navigation. However, existing approaches often struggle with data redundancy, robustness, and accuracy in complex environments. To address these challenges, we present MS-Mapping, an novel multi-session LiDAR mapping system that employs an incremental mapping scheme for robust and accurate map assembly in large-scale environments. Our approach introduces three key innovations: 1) A distribution-aware keyframe selection method that captures the subtle contributions of each point cloud frame to the map by analyzing the similarity of map distributions. This method effectively reduces data redundancy and pose graph size, while enhancing graph optimization speed; 2) An uncertainty model that automatically performs least-squares adjustments according to the covariance matrix during graph optimization, improving mapping precision, robustness, and flexibility without the need for scene-specific parameter tuning. This uncertainty model enables our system to monitor pose uncertainty and avoid ill-posed optimizations, thereby increasing adaptability to diverse and challenging environments. 3) To ensure fair evaluation, we redesign baseline comparisons and the evaluation benchmark. Direct assessment of map accuracy demonstrates the superiority of the proposed MS-Mapping algorithm compared to state-of-the-art methods. In addition to employing public datasets such as Urban-Nav, FusionPortable, and Newer College, we conducted extensive experiments on such a large \SI{855}{m}$\times$\SI{636}{m} ground truth map, collecting over \SI{20}{km} of indoor and outdoor data across more than ten sequences...