A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques
Accurate 6D object pose estimation is critical for autonomous docking. To address the inefficiencies and inaccuracies associated with maximal cliques-based pose estimation methods, we propose a fast 6D pose estimation algorithm that integrates feature space and space compatibility constraints. The a...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/1/283 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548954496401408 |
|---|---|
| author | Zhenqi Han Lizhuang Liu |
| author_facet | Zhenqi Han Lizhuang Liu |
| author_sort | Zhenqi Han |
| collection | DOAJ |
| description | Accurate 6D object pose estimation is critical for autonomous docking. To address the inefficiencies and inaccuracies associated with maximal cliques-based pose estimation methods, we propose a fast 6D pose estimation algorithm that integrates feature space and space compatibility constraints. The algorithm reduces the graph size by employing Laplacian filtering to resample high-frequency signal nodes. Then, the truncated Chamfer distance derived from fusion features and spatial compatibility constraints is used to evaluate the accuracy of candidate pose alignment between source and reference point clouds, and the optimal pose transformation matrix is selected for 6D pose estimation. Finally, a point-to-plane ICP algorithm is applied to refine the 6D pose estimation for autonomous docking. Experimental results demonstrate that the proposed algorithm achieves recall rates of 94.5%, 62.2%, and 99.1% on the 3DMatch, 3DLoMatch, and KITTI datasets, respectively. On the autonomous docking dataset, the algorithm yields rotation and localization errors of 0.96° and 5.82 cm, respectively, outperforming existing methods and validating the effectiveness of our approach. |
| format | Article |
| id | doaj-art-de43e001646d4a2da15fa88a9f651246 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-de43e001646d4a2da15fa88a9f6512462025-01-10T13:21:28ZengMDPI AGSensors1424-82202025-01-0125128310.3390/s25010283A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal CliquesZhenqi Han0Lizhuang Liu1School of Information Science and Technology, Fudan University, Shanghai 200438, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaAccurate 6D object pose estimation is critical for autonomous docking. To address the inefficiencies and inaccuracies associated with maximal cliques-based pose estimation methods, we propose a fast 6D pose estimation algorithm that integrates feature space and space compatibility constraints. The algorithm reduces the graph size by employing Laplacian filtering to resample high-frequency signal nodes. Then, the truncated Chamfer distance derived from fusion features and spatial compatibility constraints is used to evaluate the accuracy of candidate pose alignment between source and reference point clouds, and the optimal pose transformation matrix is selected for 6D pose estimation. Finally, a point-to-plane ICP algorithm is applied to refine the 6D pose estimation for autonomous docking. Experimental results demonstrate that the proposed algorithm achieves recall rates of 94.5%, 62.2%, and 99.1% on the 3DMatch, 3DLoMatch, and KITTI datasets, respectively. On the autonomous docking dataset, the algorithm yields rotation and localization errors of 0.96° and 5.82 cm, respectively, outperforming existing methods and validating the effectiveness of our approach.https://www.mdpi.com/1424-8220/25/1/283maximal cliquespose estimationautonomous dockingspatial compatibilitygraph filtering |
| spellingShingle | Zhenqi Han Lizhuang Liu A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques Sensors maximal cliques pose estimation autonomous docking spatial compatibility graph filtering |
| title | A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques |
| title_full | A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques |
| title_fullStr | A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques |
| title_full_unstemmed | A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques |
| title_short | A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques |
| title_sort | 6d object pose estimation algorithm for autonomous docking with improved maximal cliques |
| topic | maximal cliques pose estimation autonomous docking spatial compatibility graph filtering |
| url | https://www.mdpi.com/1424-8220/25/1/283 |
| work_keys_str_mv | AT zhenqihan a6dobjectposeestimationalgorithmforautonomousdockingwithimprovedmaximalcliques AT lizhuangliu a6dobjectposeestimationalgorithmforautonomousdockingwithimprovedmaximalcliques AT zhenqihan 6dobjectposeestimationalgorithmforautonomousdockingwithimprovedmaximalcliques AT lizhuangliu 6dobjectposeestimationalgorithmforautonomousdockingwithimprovedmaximalcliques |