高速移動車両におけるレーダーを用いた複数物体追跡に向けて：周波数領域でのレーダー処理の可能性
Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain
レーダーデータを周波数領域で処理することで、ノイズや構造的誤差に対するロバスト性を高め、車両の自己運動と複数の移動物体を同時に扱えることを示した論文。自動運転レースの追い越しを例に、レーダーのみでのオドメトリを実証している。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Tim Hansen, Arturo Gomez-Chavez, Ilya Shimchik, Andreas Birk
分類: cs.RO, cs.AI, cs.CV, eess.IV, eess.SP
原文アブストラクト
We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especially in comparison to feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In addition to the high robustness, the processing in the frequency domain has the so far neglected advantage that the underlying correlation based methods used for, e.g., registration, provide information about all moving structures in the scene. A typical automotive application case is overtaking maneuvers, which in the context of autonomous racing are used here as a motivating example. Initial experiments and results with Fourier SOFT in 2D (FS2D) are presented that use the Boreas dataset to demonstrate radar-only-odometry, i.e., radar-odometry without sensor-fusion, to support our arguments.
