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

Julian Gaal; Thomas Wiemann; Alexander Mock; Mario Porrmann
In: Proccedings of the 2023 IEEE International Conference on Robotic Computing. IEEE International Conference on Robotic Computing (IRC-2023), IEEE, 2023.


This paper presents FeatSense, a feature-based GPU-accelerated SLAM system for high-resolution LiDARs, combined with a map generation algorithm for real-time generation of large Truncated Signed Distance Fields (TSDFs) on embedded hardware. FeatSense uses LiDAR point cloud features for odometry estimation and point cloud registration. The registered point clouds are integrated into a global Truncated Signed Distance Field (TSDF) representation. FeatSense is intended to run on embedded systems with integrated GPU-accelerator like NVIDIA Jetson boards. In this paper, we present a realtime capable TSDF-SLAM system specially tailored for closely coupled CPU/GPU systems. The implementation is evaluated in various structured and unstructured environments and benchmarked against existing reference datasets. The main contribution of this paper is the ability to register up to 128 scan lines of an Ouster OS1-128 LiDAR at 10Hz on a NVIDIA AGX Xavier while achieving a TSDF map generation speedup by a factor of 100 compared to previous work on the same power budget.