Abstract
Robust and quick road curb detection under various situations is critical in developing intelligent vehicles. However, the road curb detection is easily affected by the obstacles in the road area when Lidar based method is applied. A practical road curb detection method using point cloud from a three-dimensional Lidar for autonomous vehicle is reported in this paper. First, a multi-feature, loose-threshold, varied-scope ground segmentation method is presented to increase the robustness of ground segmentation with which obstacles above the ground can be detected. Second, the road curb is detected by applying the global road trend and an extraction-update mechanism. Experiments show the robustness and efficiency of the road curb detection under various environments. The road curb detection method is 10 times the speed of traditional method and the accuracy is much higher than existing methods.
Highlights
Environment perception is an indispensable component for autonomous vehicle as it provides real-time environment information
In recent researches on autonomous vehicles, 3D Lidar plays a more and more import role as it provides sufficient information, in the form of point cloud, to model the 3D environment. 3D Lidar is a kind of multi-beam Lidar which percept the environment by transmitting and receiving multiple laser rays
A trend of road curb into consideration, is proposed in this study and shown in Figure 1. multiFirst, feature, loose-threshold, varied-scope ground segmentation method is presented in this work
Summary
Environment perception is an indispensable component for autonomous vehicle as it provides real-time environment information. All the aforementioned methods shared growing, and the road curb points are obtained by searching. They are considerable number of vehicles, because of the lack of the overall road shape information They are based on local features of the road rather than the global road trend. MultiFirst, feature, loose-threshold, varied-scope ground segmentation method is presented in this work. A multi-feature, loose-threshold, varied-scope ground segmentation method is presented in this. A multi-feature, loose-threshold, scope groundground segmentation method is presented to increase to theincrease robustness ground segmentation. In three aspects, namely, the shape of the road curb, the fit degree between the road curb and the Theand succeeding portions of this paperand are history organized as follows: The segmentation of the ground scene, the variance between the curve information.
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