This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis represents channels -laser beams. Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and evaluated, using a manually annotated dataset we prepared. The results show significant improvement of performance over the state-ofthe-art method by Zhang et al. in terms of speed and also minor improvements in terms of accuracy.
Velaset et al. REF segmented the ground and non-ground points by employing a convolutional neural network (CNN) framework.
CNN for very fast ground segmentation in velodyne LiDAR data
2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Engineering Computer Science