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.

翻译：本文提出了一种新颖的多模态多目标跟踪算法，用于自动驾驶汽车，该算法融合了相机与激光雷达数据。对于相机图像，采用先进的3D目标检测器进行处理；而激光雷达观测数据则通过经典聚类技术处理。所提出的多目标跟踪算法包含三步关联过程、用于估计每个动态障碍物运动的扩展卡尔曼滤波，以及轨迹管理阶段。扩展卡尔曼滤波运动模型需输入当前观测目标的相对位置与朝向，以及自车的纵向与角速度。与大多数前沿多模态多目标跟踪方法不同，本算法不依赖地图或自车全局位姿信息。此外，其仅针对相机使用3D检测器，并独立于所采用的激光雷达传感器类型。该算法在仿真环境与真实数据中均得到验证，效果令人满意。