自動運転車における前方動的物体の3D追跡フレームワーク
A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars
単眼カメラとYOLOv3・OpenCVトラッカー、非線形SFMモデルと切替型SDREフィルタを組み合わせ、前方動的物体の3D位置を推定・追跡する手法を提案し、Jetson TX2上の実時間実験で検証した。
著者: Faraz Lotfi, Hamid D. Taghirad
分類: cs.RO, cs.CV, eess.SP
原文アブストラクト
Both recognition and 3D tracking of frontal dynamic objects are crucial problems in an autonomous vehicle, while depth estimation as an essential issue becomes a challenging problem using a monocular camera. Since both camera and objects are moving, the issue can be formed as a structure from motion (SFM) problem. In this paper, to elicit features from an image, the YOLOv3 approach is utilized beside an OpenCV tracker. Subsequently, to obtain the lateral and longitudinal distances, a nonlinear SFM model is considered alongside a state-dependent Riccati equation (SDRE) filter and a newly developed observation model. Additionally, a switching method in the form of switching estimation error covariance is proposed to enhance the robust performance of the SDRE filter. The stability analysis of the presented filter is conducted on a class of discrete nonlinear systems. Furthermore, the ultimate bound of estimation error caused by model uncertainties is analytically obtained to investigate the switching significance. Simulations are reported to validate the performance of the switched SDRE filter. Finally, real-time experiments are performed through a multi-thread framework implemented on a Jetson TX2 board, while radar data is used for the evaluation.
