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Open AccessArticle

Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

1
Department of Smart Vehicle Engineering, Konkuk university, Seoul 05029, Korea
2
Department of Automotive Engineering, Hanyang university, Seoul 04763, Korea
3
Driving Assistance Research Center, Valeo, CEDEX 93012 Bobigny, France
4
Robotics and Intelligent Transportation Systems Team, INRIA Paris-Rocquencourt, 78153 Le Chesnay, France
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4119; https://doi.org/10.3390/s18124119
Received: 30 October 2018 / Revised: 16 November 2018 / Accepted: 19 November 2018 / Published: 23 November 2018
(This article belongs to the Special Issue Sensors Applications in Intelligent Vehicle)
Nowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment. View Full-Text
Keywords: Dempster-Shafer theory; occupancy grid map; cloud service; multi-vehicle mapping; LiDAR Dempster-Shafer theory; occupancy grid map; cloud service; multi-vehicle mapping; LiDAR
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MDPI and ACS Style

Jo, K.; Cho, S.; Kim, C.; Resende, P.; Bradai, B.; Nashashibi, F.; Sunwoo, M. Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping. Sensors 2018, 18, 4119.

AMA Style

Jo K, Cho S, Kim C, Resende P, Bradai B, Nashashibi F, Sunwoo M. Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping. Sensors. 2018; 18(12):4119.

Chicago/Turabian Style

Jo, Kichun; Cho, Sungjin; Kim, Chansoo; Resende, Paulo; Bradai, Benazouz; Nashashibi, Fawzi; Sunwoo, Myoungho. 2018. "Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping" Sensors 18, no. 12: 4119.

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