Back to articles
Articles
Volume: 32 | Article ID: art00020
Image
Multi-Sensor Fusion in Dynamic Environment using Evidential Grid Mapping
  DOI :  10.2352/ISSN.2470-1173.2020.16.AVM-255  Published OnlineJanuary 2020
Abstract

Grid mapping is widely used to represent the environment surrounding a car or a robot for autonomous navigation. This paper describes an algorithm for evidential occupancy grid (OG) mapping that fuses measurements from different sensors, based on the Dempster-Shafer theory, and is intended for scenes with stationary and moving (dynamic) objects. Conventional OGmapping algorithms tend to struggle in the presence of moving objects because they do not explicitly distinguish between moving and stationary objects. In contrast, evidential OG mapping allows for dynamic and ambiguous states (e.g. a LIDAR measurement: cannot differentiate between moving and stationary objects) that are more aligned with measurements made by sensors. In this paper, we present a framework for fusing measurements as they are received from disparate sensors (e.g. radar, camera and LIDAR) using evidential grid mapping. With this approach, we can form a live map of the environment, and also alleviate the problem of having to synchronize sensors in time. We also designed a new inverse sensor model for radar that allows us to extract more information from object level measurements, by incorporating knowledge of the sensor’s characteristics. We have implemented our algorithm in the OpenVX framework to enable seamless integration into embedded platforms. Test results show compelling performance especially in the presence of moving objects.

Subject Areas :
Views 17
Downloads 2
 articleview.views 17
 articleview.downloads 2
  Cite this article 

G. M. Dilshan P. Godaliyadda, Vijay Pothukuchi, JuneChul Roh, "Multi-Sensor Fusion in Dynamic Environment using Evidential Grid Mappingin Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2020,  pp 255-1 - 255-7,  https://doi.org/10.2352/ISSN.2470-1173.2020.16.AVM-255

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2020
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA