往って還って：実世界応用のためのレーダーデータシミュレーション学習
There and Back Again: Learning to Simulate Radar Data for Real-World Applications
標高マップから忠実なレーダー観測を生成する順モデルと、実レーダーから標高マップを推定する逆モデルを敵対的学習とサイクル整合性で同時最適化し、シミュレーションのみで学習したセグメンテーションモデルが実世界で実データ学習に匹敵する性能を達成した。
著者: Rob Weston, Oiwi Parker Jones, Ingmar Posner
分類: cs.RO, cs.CV, cs.LG, eess.SP
原文アブストラクト
Simulating realistic radar data has the potential to significantly accelerate the development of data-driven approaches to radar processing. However, it is fraught with difficulty due to the notoriously complex image formation process. Here we propose to learn a radar sensor model capable of synthesising faithful radar observations based on simulated elevation maps. In particular, we adopt an adversarial approach to learning a forward sensor model from unaligned radar examples. In addition, modelling the backward model encourages the output to remain aligned to the world state through a cyclical consistency criterion. The backward model is further constrained to predict elevation maps from real radar data that are grounded by partial measurements obtained from corresponding lidar scans. Both models are trained in a joint optimisation. We demonstrate the efficacy of our approach by evaluating a down-stream segmentation model trained purely on simulated data in a real-world deployment. This achieves performance within four percentage points of the same model trained entirely on real data.
