- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Knowledge Graph Search feature is available on CiNii Labs
- 【Updated on June 30, 2025】Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
6DoF-SLAM using 3D Point Cloud-based Objects Recognition
-
- Wang Jiayi
- Yokohama National University
-
- Fujimoto Yasutaka
- Yokohama National University
-
- Iwanaga Yoshihiro
- KOMATSU
-
- Miyamoto Shunsuke
- KOMATSU
Search this article
Description
<p>A method for three-dimensional (3D) point cloud-based object recognition and a method that uses the recognized objects for six-degree-of-freedom simultaneous localization and mapping (SLAM) with a high accuracy are presented. For object recognition, we use a convolutional neural network to identify the meaning of each point inside an input 3D point cloud. For scan registration, we present a highly accurate hybrid method that combines the iterative closest point with particle swarm optimization (PSO) to match the recognized points to be archived. Using PSO to match the recognized object's points in each neighboring scan can help decrease incorrect correspondences and enhance the robustness of scan matching. Compared to state-of-art methods, the proposed method achieved good performance on the KITTI odometry benchmark and our SLAM experiments.</p>
Journal
-
- IEEJ Journal of Industry Applications
-
IEEJ Journal of Industry Applications 11 (6), 752-762, 2022-11-01
The Institute of Electrical Engineers of Japan