Road edge detection on 3D point cloud data using Encoder-Decoder Convolutional Network

Abstract

The demand of High Definition Maps (HD-Maps) has been increasing, especially for autonomous vehicle application. Usually, HD-Map is created by scanning the road using LiDAR sensor and reconstructing the road on 3D world to capture all aspects of road properties. One of the important properties of a road is its edge or boundary. In this paper, we propose end-to-end 3D Encoder-Decoder Convolutional Network (3D-EDCN) for road edge detection on 3D point cloud data produced by LiDAR sensor. Our 3D-EDCN classifier consists of nine convolutional layers and three deconvolutional layers. For simplification, we use 3D voxel format as input and output of the classifier. Our proposed method was tested using our own 3D point cloud dataset which taken from LiDAR equipment and consisting of 103 3D point cloud data with their respective road edge ground truth. In the training process, we use combinations of Cross-Entropy loss function and Euclidean loss function to help our model converged. As a preliminary result, our proposed 3D-EDCN classifier achieves Mean Square Error (MSE) of 2.738×10−5, precision of 0.37262, and recall of 0.14432.