A Comparison of Global Optimizers Applied to Point Cloud Registration

Ahstract- The registration of point cloud data is essential in various applications, such as computer vision and robotics. The Iterative Closest Point (ICP) algorithm offered a solution to this problem, with several subsequent methods addressing problems including occlusions and variable point data overlap. To also account for detection errors, the Particle Swarm Optimization - Cardinalized Optimal Linear Assignment (PSO-COLA) point data registration algorithm was introduced. This algorithm offers robust registration solutions in the presence of data miss-detections and false alarms, but being based on a Particle Swarm Optimization (PSO) concept is susceptible to local minima problems. To address this problem, we propose the use of two additional meta-heuristic algorithms, namely Artificial Rabbit Optimisation (ARO) and Artificial Bee Colony (ABC), in combination with the Cardinalized Optimal Linear Assignment (COLA) metric. Our experiments show that the resulting ARO-COLA algorithm reduces the execution time compared with the former PSO-COLA algorithm while maintaining high registration accuracy, especially in scenarios with cardinality and spatial errors. The results indicate that the ARO-COLA algorithm is a promising alternative for efficient and accurate point cloud registration.