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Journal Article
DiLO: Direct Light Detection and Ranging Odometry Based on Spherical Range Images for Autonomous Driving
- Authors
- Seung-Jun Han, Jungyu Kang, Kyoung-Wook Min, Jungdan Choi
- Issue Date
- 2021-08
- Citation
- ETRI Journal, v.43, no.4, pp.603-616
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- 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 0.0031째/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.
- KSP Keywords
- Autonomous vehicle, Execution speed, Fast optimization, Image plane, Key points, LIght Detection And Ranging(LIDAR), LiDAR data, Optimization methods, Range Image, Three dimensional(3D), Three-dimensional point cloud
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
