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This paper presents FeatSense, a feature-based GPU-accelerated SLAM systemfor high resolution LiDARs, combined with a map generation algorithm forreal-time generation of large Truncated Signed Distance Fields (TSDFs) onembedded hardware. FeatSense uses LiDAR point cloud features for odometryestimation and point cloud registration. The registered point clouds areintegrated into a global Truncated Signed Distance Field (TSDF) representation.FeatSense is intended to run on embedded systems with integratedGPU-accelerator like NVIDIA Jetson boards. In this paper, we present areal-time capable TSDF-SLAM system specially tailored for close coupled CPU/GPUsystems. The implementation is evaluated in various structured and unstructuredenvironments and benchmarked against existing reference datasets. The maincontribution of this paper is the ability to register up to 128 scan lines ofan Ouster OS1-128 LiDAR at 10Hz on a NVIDIA AGX Xavier while achieving a TSDFmap generation speedup by a factor of 100 compared to previous work on the samepower budget.

FeatSense -- A Feature-based Registration Algorithm with GPU-accelerated TSDF-Mapping Backend for NVIDIA Jetson Boards