Exploiting location information to detect light pole in mobile LiDAR point clouds

Abstract

With rapid development of light detection and ranging (LiDAR) technologies, three dimensional point clouds increasingly become a new approach to sense the world. In our previous work, light poles were detected from mobile LiDAR point clouds without using their locations. In this paper, we improve our previous work by considering location information between two neighboring light poles to reduce false alarm. In the proposed method, the potential light poles are first detected by the extended Hough Forest Framework. Then, a gaussian distribution is exploited to model the distance between two light poles by using locations of those detected light poles. Finally, inaccurately detected light poles are removed by considering the distance between two adjacent objects. We evaluate our proposed method on mobile LiDAR point clouds acquired by RIEGL VMX-450 system. On the basis of the experimental test instances, we demonstrate improved accuracy on light pole detection.