Bollard Segmentation and Position Estimation From Lidar Point Cloud for Autonomous Mooring

Abstract

This article presents a computer-aided object detection and localization method from lidar 3-D point cloud data. This topic of interest is in the framework of autonomous mooring, where the ship is tied to the rigid structure on-shore (bollard) for autonomous maritime navigation. Using shape and features priors, unlike matching the whole object template to the experimental 3-D point cloud representation of the scene, two customized algorithms: 1) 3-D feature matching (3-DFM) and 2) mixed feature-correspondence matching (MFCM) are presented. The proposed algorithms discriminate and extract the 3-D points corresponding to the noncooperative bollard’s surface from the background, thus capable of classification, localization, and representing it using a unique coordinate in the 3-D world. The proposed algorithms are tested and validated by implementing upon an experimental dataset of 105 scenes where the bollard is at different positions and orientations with respect to lidar mounted on the robotic arm. Statistical and probabilistic-based approaches are taken into account to determine the performance of proposed algorithms. Model parameters’ estimation implies that errors resulting from the 3-DFM algorithm follow homoscedastic bimodal Gaussian distribution with individual Gaussian components having mean 0.03 and 0.09 m, and both have an equal standard deviation of 0.01 m. Furthermore, the posterior component assignment probability is used to identify and cluster the scenes that contribute to relatively larger errors. Finally, an improved algorithm, MFCM, is proposed, whose errors follow unimodal Gaussian distribution with a mean and a standard deviation of 0.03 and 0.01 m, respectively, thus mitigating the shortcomings of the former.