Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data
The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/19/6473 |
| _version_ | 1797515742888853504 |
|---|---|
| author | Tyson Phillips Tim D’Adamo Peter McAree |
| author_facet | Tyson Phillips Tim D’Adamo Peter McAree |
| author_sort | Tyson Phillips |
| collection | DOAJ |
| description | The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper makes the case for an alternative method that seeks to find the most likely solution based on available evidence. Specifically, an evidence-based metric is described that seeks to find the pose of the object that would maximise the conditional likelihood of reproducing the observed range measurements. A seedless search heuristic is also provided to find the most likely pose estimate in light of these measurements. The method is demonstrated to provide for pose estimation (2D and 3D shape poses as well as joint-space searches), object identification/classification, and platform localisation. Furthermore, the method is shown to be robust in cluttered or non-segmented point cloud data as well as being robust to measurement uncertainty and extrinsic sensor calibration. |
| first_indexed | 2024-03-10T06:52:33Z |
| format | Article |
| id | doaj.art-de35045313eb48928131476635077200 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:52:33Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-de35045313eb489281314766350772002023-11-22T16:46:40ZengMDPI AGSensors1424-82202021-09-012119647310.3390/s21196473Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud DataTyson Phillips0Tim D’Adamo1Peter McAree2School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, AustraliaThe capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper makes the case for an alternative method that seeks to find the most likely solution based on available evidence. Specifically, an evidence-based metric is described that seeks to find the pose of the object that would maximise the conditional likelihood of reproducing the observed range measurements. A seedless search heuristic is also provided to find the most likely pose estimate in light of these measurements. The method is demonstrated to provide for pose estimation (2D and 3D shape poses as well as joint-space searches), object identification/classification, and platform localisation. Furthermore, the method is shown to be robust in cluttered or non-segmented point cloud data as well as being robust to measurement uncertainty and extrinsic sensor calibration.https://www.mdpi.com/1424-8220/21/19/6473pose estimationobject classificationlocalisationperceptionLiDAR |
| spellingShingle | Tyson Phillips Tim D’Adamo Peter McAree Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data Sensors pose estimation object classification localisation perception LiDAR |
| title | Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data |
| title_full | Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data |
| title_fullStr | Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data |
| title_full_unstemmed | Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data |
| title_short | Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data |
| title_sort | maximum sum of evidence an evidence based solution to object pose estimation in point cloud data |
| topic | pose estimation object classification localisation perception LiDAR |
| url | https://www.mdpi.com/1424-8220/21/19/6473 |
| work_keys_str_mv | AT tysonphillips maximumsumofevidenceanevidencebasedsolutiontoobjectposeestimationinpointclouddata AT timdadamo maximumsumofevidenceanevidencebasedsolutiontoobjectposeestimationinpointclouddata AT petermcaree maximumsumofevidenceanevidencebasedsolutiontoobjectposeestimationinpointclouddata |