Automated detection of road manhole and sewer well covers from mobile LiDAR point clouds

Abstract—A novel object detection algorithm is developed for automatically detecting road manhole and sewer well covers from mobile light detection and ranging point clouds. This algorithm takes advantage of a marked point process of disks and rectangles to model the locations of manhole and sewer well covers and their geometric dimensions. A reversible jump Markov chain Monte Carlo algorithm is implemented for simulating the posterior distri-bution obtained using a Bayesian paradigm. The detection results obtained from the road surface point clouds acquired by a RIEGL VMX-450 system show that the manhole and sewer well covers can be detected automatically and accurately. The performance achieved using the proposed algorithm is much more accurate and effective than those of the other three existing algorithms. Index Terms—Manhole, marked point process, mobile light detection and ranging (LiDAR), point cloud, reversible jump Markov chain Monte Carlo (RJMCMC), sewer well. I