This thesis presents two solutions to the Simultaneous Localization and Mapping (SLAM) problem that share a common core. Featsense uses lidar point cloud features for odometry estimation, while Warpsense presents a GPU-accelerated Point-to-TSDF scan matching algorithm that performs localization in a high resolution, continuous Truncated Signed Distance Field (TSDF) representation of the environment. Both methods share the same mapping backend, a highly GPU-optimized TSDF generation module that allows the generation of efficient triangle meshes in post-processing.
The present thesis provides a solution to the loop closure problem in a Truncated Signed Distance Field (TSDF) based Simultaneous Localization and Mapping (SLAM) approach. Described is the design and algorithm for a loop closure detection and subsequent multi-step validation via validators developed as part of the thesis, which sorted out erroneous loop closures to provide more robust optimization of the pose graph. In addition, several strategies developed in the work are outlined for updating the volumetric TSDF map representation.