상세정보
학술지
DiLO: Direct Light Detection and Ranging Odometry Based on Spherical Range Images for Autonomous Driving
- 발행일
- 202108
- 출처
- ETRI Journal, v.43 no.4, pp.603-616
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 21HS2500, 고정밀 맵 음영 환경의 완전자율주행 네비게이션 인공지능 기술개발, 민경욱
- 초록
- 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 제안 키워드
- Autonomous vehicle, Execution speed, Fast optimization, Image plane, Key points, Lidar data, Light detection and Ranging(LiDAR), Range images, Three dimensional(3D), Three-dimensional point cloud, Translation errors
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