With the growing automation of road vehicles, the demands on their systems for environment perception increase. As one component the knowledge of one’s own pose (position and orientation) is of great importance. In the project Absolut a highly automated road based People Mover is being established and operated on a route in Leipzig. Among its focusses is the development of a localisation system that provides robust pose estimation under variable ambient conditions. This includes locations with GNSS shadowing and high environmental dynamics. The resulting system relies on information from GNSS, vehicle- and LiDAR odometry. For areas of the route where GNSS positioning cannot be guaranteed a georeferenced point cloud map is generated with offline Simultaneous Localisation and Mapping (SLAM). The paper focuses on a method to locate the vehicle in this map using a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$360^\circ $$\end{document} LiDAR. The approach is able to determine the global pose of the vehicle online to compensate GNSS-outages. The registration of the current scan in the map is done using the Normal Distribution Transform (NDT). A superimposed particle swarm optimisation (PSO) ensures a fast convergence of the initial state. The summary contains first experiments on the systems performance, as well as an outlook on key points for further development.
A Robust Localisation System for an Highly Automated People Mover Based on GNSS- and LiDAR-Data
Proceedings
2021-05-14
14 pages
Article/Chapter (Book)
Electronic Resource
German
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