Exploring Sensor Placement Optimization in Point Cloud-Derived Environment Models

作     者：Banov, Marina Pincic, Domagoj Lenac, Kristijan Susanj, Diego 

作者机构：Univ Rijeka Fac Engn Rijeka 51000 Croatia Univ Rijeka Ctr Artificial Intelligence & Cybersecur Rijeka 51000 Croatia Univ Rijeka Fac Engn Rijeka 51000 Croatia Juraj Dobrila Univ Pula Fac Engn Pula 52100 Croatia 

出 版 物：《IEEE SENSORS JOURNAL》 (IEEE Sensors J.)

年 卷 期：2024年第24卷第16期

页      面：26574-26583页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0702[理学-物理学] 

基　　金：University of Rijeka  Rijeka  Croatia [uniri-iskusni-tehnic-23-261] 

主　　题：Sensors Point cloud compression Solid modeling Sensor placement Three-dimensional displays Shape Cameras Environment modeling evolutionary algorithms point clouds sensor networks sensor placement 

摘      要：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 3-D 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 use 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 (ABC) algorithm consistently delivered superior results.