A Fully Integrated System for Hardware-accelerated TSDF SLAM with LiDAR Sensors (HATSDF SLAM)
Marc Eisoldt; Julian Gaal; Thomas Wiemann; Marcel Flottmann; Marc Rothmann; Marco Tassemeier; Mario Porrmann
In: Robotics and Autonomous Systems (RAS), Vol. 106, Pages 1-38, Elsevier, 2022.
Abstract
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 stateof-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.
@article{pub12489,
author = {
Eisoldt, Marc
and
Gaal, Julian
and
Wiemann, Thomas
and
Flottmann, Marcel
and
Rothmann, Marc
and
Tassemeier, Marco
and
Porrmann, Mario
},
title = {A Fully Integrated System for Hardware-accelerated TSDF SLAM with LiDAR Sensors (HATSDF SLAM)},
year = {2022},
volume = {106},
pages = {1--38},
journal = {Robotics and Autonomous Systems (RAS)},
publisher = {Elsevier}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz
