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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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DiLO: Direct light detection and ranging odometry based on spherical range images for autonomous driving

  • Han, Seung-Jun (Autonomous Driving Intelligence Research Section, Intelligent Robotics Research Division, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Kang, Jungyu (Autonomous Driving Intelligence Research Section, Intelligent Robotics Research Division, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Min, Kyoung-Wook (Autonomous Driving Intelligence Research Section, Intelligent Robotics Research Division, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Choi, Jungdan (Autonomous Driving Intelligence Research Section, Intelligent Robotics Research Division, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute)
  • Received : 2021.03.07
  • Accepted : 2021.06.16
  • Published : 2021.08.01
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Abstract

Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)-based direct odometry, which uses a spherical range image (SRI) that projects a three-dimensional point cloud onto a two-dimensional spherical image plane. Direct odometry is developed in a vision-based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state-of-the-art and remarkably higher speed than conventional techniques.

Keywords

Acknowledgement

This work was supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (No. 2018-0-00327, Development of fully autonomous driving navigation AI technology in high-precision map shadow environment).

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