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Lookup NU author(s): Dr Wen Xiao, Professor Jon MillsORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© Authors 2017. 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.
Author(s): Xiao W, Vallet B, Xiao Y, Mills J, Paparoditis N
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: ISPRS Geospatial Week 2017
Year of Conference: 2017
Pages: 171-178
Online publication date: 13/09/2017
Acceptance date: 20/05/2017
Date deposited: 01/11/2017
ISSN: 2194-9042
Publisher: Copernicus GmbH
URL: https://doi.org/10.5194/isprs-annals-IV-2-W4-171-2017
DOI: 10.5194/isprs-annals-IV-2-W4-171-2017
Series Title: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
