Polar-view based object detection algorithm using 3D low-channel lidar

被引：2
作者：
Kwon S.-S. [1 ]
Park T.-H. [2 ]
机构：
[1] Department of Control and Robot Engineering, Chungbuk National University
[2] Department of Electronics Engineering, Chungbuk National University
来源：
Journal of Institute of Control, Robotics and Systems | 2019年 / 25卷 / 01期
关键词：
3D low-channel lidar; Autonomous vehicle; Object detection; Polar-view;
D O I：
10.5302/J.ICROS.2019.18.0185
中图分类号：
学科分类号：
摘要：
In order for an autonomous vehicle to move, object detection is required to recognize the surrounding environment. The sensors used for object detection mainly use a camera and lidar. However, it is difficult to detect the camera because of its influence on the surrounding environment. Therefore, object detection using lidar is required. For lidar-based object detection, we mainly use a high-channel lidar with a high resolution. However, high-channel lidar is expensive and is difficult to commercialize. To solve this problem, object detection studies using low-channel lidar are underway. In this paper, we present an algorithm to find an object (vehicle or pedestrian) using three 3D low-channel lidar systems. First, we converted the data from the lidar to the polar view. Then, we input the converted polar view into YOLO v3 to predict the class and the region of interest (ROI) of the object. We used K-means to separate the background and the object from the image in the predicted ROI to find the object except for the background. Only the object area found last was converted back into 3D space to find the location of the object. © ICROS 2019.
引用
收藏
页码：56 / 62
页数：6
相关论文
共 27 条
  • [1] Girshick R., Donahue J., Darrell T., Malik J., Rich feature hierarchies for accurate object detection and semantic segmentation,, 2014 IEEE conference on computer vision and pattern recognition, pp. 580-587, (2014)
  • [2] Redmon J., Divvala S., Girshick R., Farhadi A., You only look once: unified, real-time object detection,, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, (2016)
  • [3] Ren S., He K., Girshick R., Sun J., Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 6, pp. 1137-1149, (2017)
  • [4] Liu W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C.Y., Berg A.C., SSD: Single Shot MultiBox Detector,, European Conference on Computer Vision 2016, pp. 21-37, (2016)
  • [5] Dai J., Li Y., He K., Sun J., R-FCN: Object detection via region-based fully convolutional networks,, Neural Information Processing Systems 2016, pp. 379-387, (2016)
  • [6] Chen X., Ma H., Wan J., Li B., Xia T., Multi-view 3D object detection network for autonomous driving,, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 6526-6534, (2017)
  • [7] Li B., Zhang T., Xia T., Vehicle detection from 3D lidar using fully convolutional network,
  • [8] Engelcke M., Rao D., Wang D.Z., Tong C.H., Posner I., Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks,, 2017 IEEE International Conference on Robotics and Automation, pp. 1355-1361, (2017)
  • [9] Zhou Y., Tuzel O., VoxelNet: End-to-End learning for point cloud based 3D object detection,, 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4490-4499, (2018)
  • [10] Wang D.Z., Posner I., Voting for voting in online point cloud object detection,, Robotics: Science and Systems 2015, (2015)
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