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.