A comparison of extended object tracking with multi-modal sensors in indoor environment
This incremental study addresses cost-effective sensor selection for object tracking in indoor settings, such as robotics or surveillance.
The paper tackled the problem of single object tracking in indoor environments by comparing LiDAR and stereo camera sensors, finding that the stereo camera achieved similar performance to LiDAR at a cost over ten times lower.
This paper presents a preliminary study of an efficient object tracking approach, comparing the performance of two different 3D point cloud sensory sources: LiDAR and stereo cameras, which have significant price differences. In this preliminary work, we focus on single object tracking. We first developed a fast heuristic object detector that utilizes prior information about the environment and target. The resulting target points are subsequently fed into an extended object tracking framework, where the target shape is parameterized using a star-convex hypersurface model. Experimental results show that our object tracking method using a stereo camera achieves performance similar to that of a LiDAR sensor, with a cost difference of more than tenfold.