Abstract
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.
Highlights
Intelligent vehicles, such as self-driving cars and industrial mobile robots, require surrounding environment information to perform efficient and safe driving
In the evidential occupancy grid map (EOGM) based on the DS theory, the states of the cell can be extended to the power set 2Ω = { F, O, Ω, ∅}, which is the set of all subsets of the Ω = { F, O}
This paper presents a cloud update framework of multi-vehicle EOGMs based on the GraphSLAM, geodetic quad-tree tile system, and the evidential cloud update of EOGM tiles
Summary
Intelligent vehicles, such as self-driving cars and industrial mobile robots, require surrounding environment information to perform efficient and safe driving. To overcome the limits of the POGM, evidential (Dempster-Shafer) theory has been applied to model the occupancy grid map [8,9,10]. The intelligent vehicles equipped with perception sensors (such as LiDAR and Stereo camera), GPS receiver, and motion sensors are able to create the EOGM for their passing trajectory If these vehicles have a connection to the Internet, the created EOGMs can be shared with other vehicles through a cloud map service. The main contribution of this paper is to propose a cloud update framework of evidential occupancy grid maps (EOGMs) for multiple intelligent vehicles in the large-scale real road environment.
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