HATSDF SLAM - Hardware-accelerated TSDF SLAM for Reconfigurable SoCs

Marc Eisoldt, Marcel Flottmann, Julian Gaal, Pascal Buschermöhle, Steffen Hinderink, Malte Hillmann, Adrian Nietschmann, Patrick Hoffmann, Thomas Wiemann, Mario Porrmann

In: Proceedings of ECMR 2021. European Conference on Mobile Robots (ECMR) IEEE 2021.


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 a Velodyne VLP-16 LiDAR 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.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz