自動運転のためのカメラ-LiDAR融合による複数物体追跡
Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving
カメラとLiDARのデータを組み合わせ、地図や自己位置情報に依存せずに動的障害物を追跡するマルチモーダルMOTアルゴリズムを提案し、シミュレーションと実データで検証した。
著者: Riccardo Pieroni, Simone Specchia, Matteo Corno, Sergio Matteo Savaresi
分類: cs.RO, cs.CV
原文アブストラクト
This paper presents a novel multi-modal Multi-Object Tracking (MOT) algorithm for self-driving cars that combines camera and LiDAR data. Camera frames are processed with a state-of-the-art 3D object detector, whereas classical clustering techniques are used to process LiDAR observations. The proposed MOT algorithm comprises a three-step association process, an Extended Kalman filter for estimating the motion of each detected dynamic obstacle, and a track management phase. The EKF motion model requires the current measured relative position and orientation of the observed object and the longitudinal and angular velocities of the ego vehicle as inputs. Unlike most state-of-the-art multi-modal MOT approaches, the proposed algorithm does not rely on maps or knowledge of the ego global pose. Moreover, it uses a 3D detector exclusively for cameras and is agnostic to the type of LiDAR sensor used. The algorithm is validated both in simulation and with real-world data, with satisfactory results.
