Transformer-based Monte Carlo Localization in Construction Meshes

LiDAR relocalization has attracted increasing attention as it can deliver accurate 6-DoF pose estimation in complex 3D environments. Recent learning-based regression methods offer efficient solutions by directly predicting global poses without the need for explicit map storage. However, these methods often struggle in challenging scenes due to their equal treatment of all predicted points, which is vulnerable to noise and outliers. In this paper, we propose LEADER, a robust LiDAR-based relocalization framework enhanced by a simple, yet effective geometric encoder. Specifically, a Robust Projection-based Geometric Encoder architecture which captures multi-scale geometric features is first presented to enhance descriptiveness in geometric representation. A Truncated Relative Reliability loss is then formulated to model point-wise ambiguity and mitigate the influence of unreliable predictions. Extensive experiments on the Oxford RobotCar and NCLT datasets demonstrate that LEADER outperforms state-of-the-art methods, achieving 24.1% and 73.9% relative reductions in position error over existing techniques, respectively. The source code is released on https://github.com/JiansW/LEADER.

3D global relocalization is one of the key capabilities for mobile robots in practical applications. However, in large scale spaces, existing methods often suffer from prolonged online relocalization time due to factors such as the massive pose search space and high computational overhead. To address these issues, this paper proposes an offline-online hierarchical framework that decouples the search space. In the offline phase, candidate positions and their corresponding geometric descriptor indices are generated in the map by simulating LiDAR scans within the grid map. In the online phase, a coarse pose estimate is first obtained via global retrieval, followed by point cloud registration to output precise 6-DoF pose estimates. Real-world experiments demonstrate that the proposed method achieves an average relocalization time of 3 s and an average localization accuracy of 8 cm in 3D environments. Compared with existing global relocalization methods, the proposed method achieves an order-of-magnitude improvement in computational efficiency while delivering comparable relocalization accuracy.

Global localization in geometrically aliased, quasi-static environments such as grocery stores, offices, schools, and hospitals poses a significant challenge for mobile robots. Grocery stores with parallel aisles and a long tailed distribution of products, as well as offices and labs with repetitive furniture such as chairs, desks, monitors, and doors, exemplify common indoor environments that present geometric and even semantic ambiguity. Traditional approaches rely either on distinct geometric features or on domain-specific vision pipelines that struggle with long-tail semantic distributions and transient visual clutter. We present VLM-GLoc, a method for hierarchical semantic Monte Carlo Localization (MCL) that leverages open-vocabulary Vision-Language Models (VLMs) as a unified semantic observation front-end. We hypothesize a three-fold benefit from VLMs: (1) extracting highly discriminative rich text features, (2) implicit quality filtering of blurry or dynamic objects, and (3) permanence reasoning for targeted data augmentation. We introduce an inverse semantic proposal mechanism that seeds particles via text-to-map retrieval. Evaluated across two real-world environments with different characteristics and two different platforms: a 3,500 sq. ft. grocery store with a cellphone and a 3,700 sq. ft. lab space with a quadruped, VLM-GLoc achieves 70% and 74% global localization success respectively, substantially outperforming traditional geometry-only and domain-specific baselines.