BirdNet: A 3D Object Detection Framework from LiDAR Information
Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally f...
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| Vydáno v: | Proceedings (IEEE Conference on Intelligent Transportation Systems) s. 3517 - 3523 |
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| Hlavní autoři: | , , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.11.2018
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| Témata: | |
| ISBN: | 9781728103211, 1728103215 |
| ISSN: | 2153-0017 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision. We present a LiDAR-based 3D object detection pipeline entailing three stages. First, laser information is projected into a novel cell encoding for bird's eye view projection. Later, both object location on the plane and its heading are estimated through a convolutional neural network originally designed for image processing. Finally, 3D oriented detections are computed in a post-processing phase. Experiments on KITTI dataset show that the proposed framework achieves state-of-the-art results among comparable methods. Further tests with different LiDAR sensors in real scenarios assess the multi-device capabilities of the approach. |
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| ISBN: | 9781728103211 1728103215 |
| ISSN: | 2153-0017 |
| DOI: | 10.1109/ITSC.2018.8569311 |