Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping
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 Interne...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/18/12/4119 |
| _version_ | 1811308219698511872 |
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| author | Kichun Jo Sungjin Cho Chansoo Kim Paulo Resende Benazouz Bradai Fawzi Nashashibi Myoungho Sunwoo |
| author_facet | Kichun Jo Sungjin Cho Chansoo Kim Paulo Resende Benazouz Bradai Fawzi Nashashibi Myoungho Sunwoo |
| author_sort | Kichun Jo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T09:18:16Z |
| format | Article |
| id | doaj.art-ce43c20f59664b2e8e8fd143c661a2c6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:18:16Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ce43c20f59664b2e8e8fd143c661a2c62022-12-22T02:52:41ZengMDPI AGSensors1424-82202018-11-011812411910.3390/s18124119s18124119Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle MappingKichun Jo0Sungjin Cho1Chansoo Kim2Paulo Resende3Benazouz Bradai4Fawzi Nashashibi5Myoungho Sunwoo6Department of Smart Vehicle Engineering, Konkuk university, Seoul 05029, KoreaDepartment of Automotive Engineering, Hanyang university, Seoul 04763, KoreaDepartment of Automotive Engineering, Hanyang university, Seoul 04763, KoreaDriving Assistance Research Center, Valeo, CEDEX 93012 Bobigny, FranceDriving Assistance Research Center, Valeo, CEDEX 93012 Bobigny, FranceRobotics and Intelligent Transportation Systems Team, INRIA Paris-Rocquencourt, 78153 Le Chesnay, FranceDepartment of Automotive Engineering, Hanyang university, Seoul 04763, KoreaNowadays, 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.https://www.mdpi.com/1424-8220/18/12/4119Dempster-Shafer theoryoccupancy grid mapcloud servicemulti-vehicle mappingLiDAR |
| spellingShingle | Kichun Jo Sungjin Cho Chansoo Kim Paulo Resende Benazouz Bradai Fawzi Nashashibi Myoungho Sunwoo Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping Sensors Dempster-Shafer theory occupancy grid map cloud service multi-vehicle mapping LiDAR |
| title | Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping |
| title_full | Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping |
| title_fullStr | Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping |
| title_full_unstemmed | Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping |
| title_short | Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping |
| title_sort | cloud update of tiled evidential occupancy grid maps for the multi vehicle mapping |
| topic | Dempster-Shafer theory occupancy grid map cloud service multi-vehicle mapping LiDAR |
| url | https://www.mdpi.com/1424-8220/18/12/4119 |
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