Road Segmentation on low resolution Lidar point clouds for autonomous vehicles
This addresses the cost and deployment challenges of high-resolution LiDARs in autonomous driving, though it is incremental as it builds on existing LoDNN architectures.
The paper tackles road segmentation on low-resolution LiDAR point clouds for autonomous vehicles by introducing local normal vector features, showing that this improves accuracy on full-resolution data and reduces performance degradation when subsampling to simulate cheaper 16/32-layer LiDARs, with experiments on KITTI and Semantic KITTI datasets.
Point cloud datasets for perception tasks in the context of autonomous driving often rely on high resolution 64-layer Light Detection and Ranging (LIDAR) scanners. They are expensive to deploy on real-world autonomous driving sensor architectures which usually employ 16/32 layer LIDARs. We evaluate the effect of subsampling image based representations of dense point clouds on the accuracy of the road segmentation task. In our experiments the low resolution 16/32 layer LIDAR point clouds are simulated by subsampling the original 64 layer data, for subsequent transformation in to a feature map in the Bird-Eye-View (BEV) and SphericalView (SV) representations of the point cloud. We introduce the usage of the local normal vector with the LIDAR's spherical coordinates as an input channel to existing LoDNN architectures. We demonstrate that this local normal feature in conjunction with classical features not only improves performance for binary road segmentation on full resolution point clouds, but it also reduces the negative impact on the accuracy when subsampling dense point clouds as compared to the usage of classical features alone. We assess our method with several experiments on two datasets: KITTI Road-segmentation benchmark and the recently released Semantic KITTI dataset.