Exploring Sensor Placement Optimization in Point Cloud-Derived Environment Models

In this study, we present a novel method for creating an environment model suitable for addressing the sensor placement problem. We extract a detailed environment model from a 3D point cloud by identifying spatial boundaries and furniture in indoor spaces and representing them as a series of polygons. To validate our method, we compare its performance against ground truth data, demonstrating high accuracy in both simple and complex environments. Subsequently, we employ the obtained models in a comprehensive experiment that evaluates the effectiveness of six metaheuristic optimization algorithms in solving the sensor placement problem. We examine how the choice of optimization algorithm and the number of sensors impacts the achieved coverage through statistical analysis. With this study, we gain insights into the comparative effectiveness of various evolutionary algorithms in enhancing sensor network design within indoor spaces. In particular, the Artificial Bee Colony algorithm consistently delivered superior results.