Segmentation of Vehicles and Roads by a Low-Channel Lidar
An effective method to segment vehicles and roads is proposed for autonomous vehicles using low-channel 3D lidar. The distance-view transformation is newly proposed to overcome the low density of top-view data of lidar. In addition, a dilated convolution structure is proposed to expand the receptive...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2019-11, Vol.20 (11), p.4251-4256 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Lee, Jae-Seol Jo, Jun-Hyeong Park, Tae-Hyoung |
| description | An effective method to segment vehicles and roads is proposed for autonomous vehicles using low-channel 3D lidar. The distance-view transformation is newly proposed to overcome the low density of top-view data of lidar. In addition, a dilated convolution structure is proposed to expand the receptive field of a convolutional neural network. The proposed network improves the accuracy of segmentation. The experimental results are presented to verify the usefulness of the proposed method. |
| doi_str_mv | 10.1109/TITS.2019.2903529 |
| format | Article |
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| ispartof | IEEE transactions on intelligent transportation systems, 2019-11, Vol.20 (11), p.4251-4256 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | 3D lidar Artificial neural networks Autonomous vehicle Autonomous vehicles Convolution convolution neural network Image segmentation Laser radar Lidar receptive field Roads Segmentation spherical coordinate Three-dimensional displays Two dimensional displays Vehicles |
| title | Segmentation of Vehicles and Roads by a Low-Channel Lidar |
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