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：本文提出了一种面向自动驾驶汽车的新型多模态多目标跟踪算法，该算法融合了相机与激光雷达数据。相机帧采用最先进的3D目标检测器进行处理，而激光雷达观测数据则运用经典聚类技术。所提出的多目标跟踪算法包含三步关联过程、用于估计每个检测到的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要当前观测物体的相对位置与朝向、以及自车的纵向与角速度作为输入。与大多数现有最先进的多模态多目标跟踪方法不同，该算法不依赖地图或自车全局位姿信息。此外，该算法仅使用相机的3D检测器，且激光雷达传感器的类型无关。该算法通过仿真与真实数据验证，均取得了满意的结果。