Han, Longfei (Fraunhofer Institute for Transportation and Infrastructure Syste), Xu, Qiuyu (Fraunhofer), Kefferpütz, Klaus (Technische Hochschule Ingolstadt), Elger, Gordon (Technische Hochschule Ingolstadt (University of Applied Science ), Beyerer, Jürgen (Fraunhofer Institute of Optronics, Systems Technologies and Imag)

Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors

Scheduled for presentation during the Poster Session "Accurate Positioning and Localization" (ThAT17), Thursday, September 26, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave radar sensors for large-scale traffic monitoring due to their weatherproof functionality, long sensing range and low manufacturing cost. However, the localization method using external measurement devices has limitations in urban environments. Furthermore, if the sensor mount exhibits changes due to environmental influences, they cannot be corrected when the measurement is performed only during the installation. In this paper, we propose self-localization of roadside radar data using Extended Object Tracking (EOT). The method analyses both the tracked trajectories of the vehicles observed by the sensor and the aerial laser scan of city streets, assigns labels of driving behaviors such as "straight ahead", "left turn", "right turn" to trajectory sections and road segments, and performs Semantic Iterative Closest Points (SICP) algorithm to register the point cloud. The method exploits the result from a down stream task -- object tracking -- for localization. We demonstrate high accuracy in the sub-meter range along with very small orientation error. The method also shows good data efficiency. The evaluation is done in both simulation and real-world tests.