A fully integrated system for hardware-accelerated TSDF SLAM with LiDAR sensors (HATSDF SLAM)

Simultaneous Localization and Mapping (SLAM) is one of the fundamental problems in autonomous robotics. Over the years, many approaches to solve this problem for 6D poses and 3D maps based on LiDAR sensors or depth cameras have been proposed. One of the main drawbacks of the solutions found in the l...

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Veröffentlicht in:Robotics and autonomous systems Jg. 156; S. 104205
Hauptverfasser: Eisoldt, Marc, Gaal, Julian, Wiemann, Thomas, Flottmann, Marcel, Rothmann, Marc, Tassemeier, Marco, Porrmann, Mario
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.10.2022
Schlagworte:
3D mapping
FPGA programming
Hardware acceleration
SLAM
FPGA programming
SLAM
Hardware acceleration
3D mapping
ISSN:0921-8890, 1872-793X
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Zusammenfassung:Simultaneous Localization and Mapping (SLAM) is one of the fundamental problems in autonomous robotics. Over the years, many approaches to solve this problem for 6D poses and 3D maps based on LiDAR sensors or depth cameras have been proposed. One of the main drawbacks of the solutions found in the literature is the required computational power and corresponding energy consumption. In this paper, we present an approach for LiDAR-based SLAM that maintains a global truncated signed distance function (TSDF) to represent the map. It is implemented on a System-On-Chip (SoC) with an integrated FPGA accelerator. The proposed system is able to track the position of state-of-the-art LiDARs in real time, while maintaining a global TSDF map that can be used to create a polygonal map of the environment. We show that our implementation delivers competitive results compared to state-of-the-art algorithms while drastically reducing the power consumption compared to classical CPU or GPU-based methods. •TSDF-based real time capable 6D SLAM for lidars.•Hardware implementation on a reconfigurable SoC with FPGA.•Fully pipelined implementation to maximize throughput.•Drastically reduced power consumption in comparison to classical implementations on CPUs and GPUs.•Competitive results to state-of-the-art algorithms.
ISSN:0921-8890
1872-793X
DOI:10.1016/j.robot.2022.104205