Volume IV-2/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 171-178, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-171-2017
© 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, 171-178, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-171-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

OCCUPANCY MODELLING FOR MOVING OBJECT DETECTION FROM LIDAR POINT CLOUDS: A COMPARATIVE STUDY

W. Xiao1, B. Vallet2, Y. Xiao3, J. Mills1, and N. Paparoditis2 W. Xiao et al.
  • 1School of Engineering, NEOlab, Newcastle University, Newcastle upon Tyne, UK
  • 2Université Paris-Est, LaSTIG MATIS, IGN, ENSG, Saint-Mandé, France
  • 3Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, USA

Keywords: Laser Scanning, Change Detection, Dynamic Scene, Occupancy Grid, Probability, Dempster-Shafer Theory

Abstract. Lidar technology has been widely used in both robotics and geomatics for environment perception and mapping. Moving object detection is important in both fields as it is a fundamental step for collision avoidance, static background extraction, moving pattern analysis, etc. A simple method involves checking directly the distance between nearest points from the compared datasets. However, large distances may be obtained when two datasets have different coverages. The use of occupancy grids is a popular approach to overcome this problem. There are two common theories employed to model occupancy and to interpret the measurements, Dempster- Shafer theory and probability. This paper presents a comparative study of these two theories for occupancy modelling with the aim of moving object detection from lidar point clouds. Occupancy is modelled using both approaches and their implementations are explained and compared in details. Two lidar datasets are tested to illustrate the moving object detection results.