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 real-time capable TSDF-SLAM system specially tailored for close 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.

翻译：本文提出FeatSense，一种基于特征的GPU加速SLAM系统，适用于高分辨率激光雷达，并配备嵌入式硬件上实时生成大规模截断符号距离场（TSDF）的建图算法。FeatSense利用激光雷达点云特征进行里程计估计与点云配准，将配准后的点云融入全局截断符号距离场（TSDF）表示中。该系统旨在运行于集成GPU加速器的嵌入式平台（如NVIDIA Jetson板卡）。本文介绍了一种专为紧耦合CPU/GPU系统设计的实时TSDF-SLAM系统，并在多种结构化及非结构化环境中进行实现评估，同时与现有参考数据集进行基准测试。本文的主要贡献在于：在NVIDIA AGX Xavier平台上，可对Ouster OS1-128激光雷达的128条扫描线实现10Hz配准频率，同时TSDF建图速度相比同功耗预算下的先前工作提升100倍。