Metaheuristics for the Optimization of Terrestrial LiDAR Set-Up

3D point clouds have a significant impact on a wide range of applications, although their acquisition is frequently conditioned by the occlusion of the objects in the scene. To address this problem, this paper describes an approach for optimizing LiDAR (Light Detection and Ranging) surveys using metaheuristics such as local searches and genetic algorithms. The method generates a set of optimal scanning locations to densely cover the real-world environment represented through 3D synthetic models. Compared to previous research, this paper handles 3D occlusion by varying the height of the sensor. Also, previously used metrics are compressed into three functions to avoid multi-objective optimization. Regarding performance, a LiDAR scanning solution based on GPU (Graphics Processing Unit) hardware is used. Several tests were conducted to show that the combination of local searches and genetic algorithms generates a reduced set of locations capable of optimizing the scanning of buildings.