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.

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，该算法结合了相机和 LiDAR 数据。通过使用先进的 3D 物体检测器处理相机帧，以及使用经典的聚类技术处理 LiDAR 观察数据。该算法由三个步骤的联合过程、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器，以及轨迹管理阶段组成。与大多数先进的多模态目标跟踪方法不同，该算法不依赖地图或车辆全局姿态的知识。此外，它专门针对相机使用 3D 检测器，并且对使用的 LiDAR 传感器类型不敏感。该算法在仿真环境和实际数据中进行验证，并且结果令人满意。