Efficient Rectangle Fitting of Sparse Laser Data for Robust On-Road Obiect Detection

On-road object detection is one of the most important tasks for the autonomous driving of intelligent vehicle. Nevertheless, the previous methods based on 2D LIDAR sensor only focus on the detection of vehicles, and show severe limitations on the detection of other objects. Accordingly, this paper proposes an on-road object detection method, which employs rectangle fitting and concavity determination to improve the robustness of ob- ject detection. The proposed approaches are extensively evaluated by using the sparse laser data collected by 2D LIDAR from real traffic environment. Experimental results demonstrate that the proposed rectangle fitting outperforms the previous approaches in terms of both detection accuracy and computational efficiency.