Boundary detection with a road model for occupancy grids in the curvilinear coordinate system using a downward-looking lidar sensor

Many studies using a laser scanner have been conducted in order to study the environment of vehicles in real time. The method to find the driving area using a two-dimensional lidar sensor is divided into a forward-looking lidar sensor and a downward-looking lidar sensor based on the installation met...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part D, Journal of automobile engineering Journal of automobile engineering, 2016-09, Vol.230 (10), p.1351-1363
Main Authors: Kim, Je Seok, Jeong, Jin Han, Park, Jahng Hyon
Format: Article
Language:English
Subjects:
Automobiles
Automotive engineering
Boundaries
Experiments
Lidar
Mathematical models
Position sensing
Real time
Roads
Roads & highways
Sensors
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Summary:Many studies using a laser scanner have been conducted in order to study the environment of vehicles in real time. The method to find the driving area using a two-dimensional lidar sensor is divided into a forward-looking lidar sensor and a downward-looking lidar sensor based on the installation method. A downward-looking lidar sensor looks at the ground, enabling it to recognize kerbs and ditches which are lower than the installation position of the sensor. However, a downward-looking lidar sensor requires pre-processing to find the road boundary. The existing sensor models cannot generate an occupancy grid map without support, as the driving area recognized through a downward-looking lidar sensor forms a circular sector shape from the sensor installation position to the road boundary. This paper proposes a road sensor model that is capable of modelling an occupancy grid. We also propose a method to generate an occupancy grid map more suitable for autonomous vehicles by presenting the occupancy grid map in curvilinear space. The proposed method was validated by an experiment at Hanyang University campus and the quantitative results obtained from that experiment. We also compared this method with three conventional sensor model methods. The experimental results show that our method performs better than the conventional methods do in terms of both visual qualities and metric qualities.
ISSN:0954-4070
2041-2991
DOI:10.1177/0954407015608051