Volume IV-2/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 75-82, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-75-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 75-82, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-75-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2017

12 Sep 2017

A COMPARISON OF SIMULATED ANNEALING, GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION IN OPTIMAL FIRST-ORDER DESIGN OF INDOOR TLS NETWORKS

F. Jia and D. Lichti F. Jia and D. Lichti
  • Dept. of Geomatics Engineering, University of Calgary, Calgary, AB, Canada

Keywords: Simulated Annealing Algorithm, Genetic Algorithm, Particle Swarm Optimization, Optimal TLS Network Design

Abstract. The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn’t guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.