Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving
Riccardo Pieroni Simone Specchia Matteo Corno Sergio Matteo Savaresi
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.