Odometry Estimation via CNN using Sparse LiDAR Data

Hae Min Cho, HyungGi Jo, Seongwon Lee, Euntai Kim

Abstract: 3D depth sensors such as LiDAR have proven to be very useful in recognizing the surrounding environment for the past decade, but the methods using these 3D data directly is not very much. Especially, there is few methods exist to use deep learning because of the sparseness characteristic of the LiDAR data. This paper presents the odometry estimation of sparse LiDAR(3D laser scanning) data using deep learning. We first voxelize the given consecutive LiDAR scans and concatenate the voxelized data to make input tensor. Then we train the 3D convolutional neural networks with the tensor to calculate 6DoF pose between the LiDAR scans. The proposed method can replace position information of the wheel encoders of GPS data when the data is absenting.