Exploiting 3D Grids for Indoor SLAM in Featureless Scenarios

Accurate multi-sensor localization is a challenging task in the navigation of AMRs. Precise localization strategies are essential for AMRs to be able to perform with safety their missions in their surrounding environments. This work proposes a novel ROS-based modular 3D grid-based particle filter-based framework that can be used for Simultaneous Localization and Mapping (SLAM) or as a standalone robust localization strategy. The framework uses odometry and 3D LiDAR data as inputs for localization and SLAM. To further improve localization and representation alignment, a pose refinement stage is employed using Levenberg-Marquardt minimization. The refinement stage considers keypoints in the environment to improve localization and uses the raw 3D point cloud for map maintenance. A pyramid-like 3D grid resolution is used to aid the refinement of the representation, improving pose estimates in featureless scenarios. Experimental validation was carried out with data acquired using an in-house platform, in a set of indoor and semi-structured scenarios comprised of critical featureless areas. The obtained results highlight the robustness of the proposed framework in both SLAM and localization tasks. The code (ROS package) is made available in a GitHub repository
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