Odometry Estimation via CNN using Sparse LiDAR Data

Hae Min Cho, Hyung Gi Jo, Seongwon Lee, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2019 16th International Conference on Ubiquitous Robots, UR 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages124-127
Number of pages4
ISBN (Electronic)9781728132327
DOIs
Publication statusPublished - 2019 Jun
Event16th International Conference on Ubiquitous Robots, UR 2019 - Jeju, Korea, Republic of
Duration: 2019 Jun 242019 Jun 27

Publication series

Name2019 16th International Conference on Ubiquitous Robots, UR 2019

Conference

Conference16th International Conference on Ubiquitous Robots, UR 2019
CountryKorea, Republic of
CityJeju
Period19/6/2419/6/27

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Mechanical Engineering
  • Control and Optimization

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  • Cite this

    Cho, H. M., Jo, H. G., Lee, S., & Kim, E. (2019). Odometry Estimation via CNN using Sparse LiDAR Data. In 2019 16th International Conference on Ubiquitous Robots, UR 2019 (pp. 124-127). [8768571] (2019 16th International Conference on Ubiquitous Robots, UR 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URAI.2019.8768571