Point Cloud Data Registration for Outdoor and Unstructured Environments

Point cloud registration is crucial for applications in computer vision and robotics. The Iterative Closest Point (ICP) algorithm has been a key, but limited, solution for this problem, with subsequent methods having been devised to address occlusions and variable point overlap. To cope with detection errors, the Particle Swarm Optimization (PSO)-Cardinalized Optimal Linear Assignment (COLA) algorithm was introduced, providing robustness against point data missed detections and false alarms showing significant performance improvements in data sets with limited overlap. However, since PSO is based on particle swarm optimization, it can be affected by local minima problems. To mitigate these issues, the Artificial Rabbit Optimization (ARO)-COLA algorithm is used in this article, incorporating the ARO approach together with the COLA metric. In this paper, the ARO-COLA and PSO-COLA algorithms will be applied to the challenging outdoor “Wood Summer” dataset, comparing their performances with other state-of-the-art methods. The results will show that with such unstructured data sets, the ARO-COLA registration algorithm outperforms most state-of-the-art registration methods, achieving similar accuracy to its PSO-COLA registration predecessor, but with improvements in runtime.