スケーラブルなレーダー路側知覚：交通分析のための自己位置推定と占有ヒートマップ
Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis
4Dミリ波レーダーを用いた路側知覚において、車両軌跡と航空レーザースキャンをICPで位置合わせする自己位置推定法を提案し、都市規模の占有ヒートマップを生成して交通分析に応用した。
著者: Longfei Han, Qiuyu Xu, Klaus Kefferpütz, Ying Lu, Gordon Elger, Jürgen Beyerer
分類: cs.RO
原文アブストラクト
4D mmWave radar sensors are suitable for roadside perception in city-scale Intelligent Transportation Systems (ITS) due to their long sensing range, weatherproof functionality, simple mechanical design, and low manufacturing cost. In this work, we investigate radar-based ITS for scalable traffic analysis. Localization of these radar sensors at city scale is a fundamental task in ITS. For flexible sensor setups, it requires even more effort. To address this task, we propose a self-localization approach that matches two descriptions of the "road": the one from the geometry of the motion trajectories of cumulatively observed vehicles, and the other one from the aerial laser scan. An Iterative Closest Point (ICP) algorithm is used to register the motion trajectory in the road section of the laser scan. The resulting estimate of the transformation matrix represents the sensor pose in a global reference frame. We evaluate the results and show that it outperforms other map-based radar localization methods, especially for the orientation estimation. Beyond the localization result, we project radar sensor data onto a city-scale laser scan and generate a scalable occupancy heat map as a traffic analysis tool. This is demonstrated using two radar sensors monitoring an urban area in the real world.
