Paper Info
Title
Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids.
Abstract
Evidential grids have recently shown interesting properties for mobile object perception. Evidential grids are a generalisation of Bayesian occupancy grids using Dempster- Shafer theory. In particular, these grids can handle efficiently partial information. The novelty of this article is to propose a perception scheme enhanced by geo-referenced maps used as an additional source of information, which is fused with a sensor grid. The paper presents the key stages of such a data fusion process. An adaptation of conjunctive combination rule is presented to refine the analysis of the conflicting information. The method uses temporal accumulation to make the distinction between stationary and mobile objects, and applies contextual discounting for modelling information obsolescence. As a result, the method is able to better characterise the occupied cells by differentiating, for instance, moving objects, parked cars, urban infrastructure and buildings. Experiments carried out on real- world data illustrate the benefits of such an approach.
Year
Venue
Field
2014
intelligent robots and systems
Data mining,Obsolescence,Discounting,Generalization,Sensor fusion,Engineering,Novelty,Sensor grid,Perception,Bayesian probability
DocType
Volume
ISSN
Journal
abs/1401.5657
IEEE/RSJ International Conference on Intelligent Robots and Systems. 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Tokyo : Japan (2013)
Citations 
PageRank 
References 
0
0.34
6
Authors
4
Name
Order
Citations
PageRank
Marek Kurdej1201.83
Julien Moras2795.77
Véronique Cherfaoui315016.92
Philippe Bonnifait445255.82