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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.12989 |
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| _version_ | 1866913581313818624 |
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| author | Castorena, Juan Dickman, L. Turin Killebrew, Adam J. Gattiker, James R Linn, Rod Loudermilk, E. Louise |
| author_facet | Castorena, Juan Dickman, L. Turin Killebrew, Adam J. Gattiker, James R Linn, Rod Loudermilk, E. Louise |
| contents | Access to highly detailed models of heterogeneous forests, spanning from the near surface to above the tree canopy at varying scales, is increasingly in demand. This enables advanced computational tools for analysis, planning, and ecosystem management. LiDAR sensors, available through terrestrial (TLS) and aerial (ALS) scanning platforms, have become established as the primary technologies for forest monitoring due to their capability to rapidly collect precise 3D structural information. Forestry now recognizes the benefits that a multi-scale approach can bring by leveraging the strengths of each platform. Here, we propose ForestAlign: an effective, target-less, and fully automatic co-registration method for aligning forest point clouds collected from multi-view, multi-scale LiDAR sources. ForestAlign employs an incremental alignment strategy, grouping and aggregating 3D points based on increasing levels of structural complexity. This strategy aligns 3D points from less complex (e.g., ground) to more complex structures (e.g., tree trunks, foliage) sequentially, refining alignment iteratively. Empirical evidence demonstrates the method's effectiveness in aligning scans, with RMSE errors of less than 0.75 degrees in rotation and 5.5 cm in translation in the TLS to TLS case and of 0.8 degrees and 8 cm in the TLS to ALS case, respectively. These results demonstrate that ForestAlign can effectively integrate TLS-to-TLS and TLS-to-ALS forest scans, making it a valuable tool in GPS-denied areas without relying on manually placed targets, while achieving high performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_12989 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ForestAlign: Automatic Forest Structure-based Alignment for Multi-view TLS and ALS Point Clouds Castorena, Juan Dickman, L. Turin Killebrew, Adam J. Gattiker, James R Linn, Rod Loudermilk, E. Louise Robotics Access to highly detailed models of heterogeneous forests, spanning from the near surface to above the tree canopy at varying scales, is increasingly in demand. This enables advanced computational tools for analysis, planning, and ecosystem management. LiDAR sensors, available through terrestrial (TLS) and aerial (ALS) scanning platforms, have become established as the primary technologies for forest monitoring due to their capability to rapidly collect precise 3D structural information. Forestry now recognizes the benefits that a multi-scale approach can bring by leveraging the strengths of each platform. Here, we propose ForestAlign: an effective, target-less, and fully automatic co-registration method for aligning forest point clouds collected from multi-view, multi-scale LiDAR sources. ForestAlign employs an incremental alignment strategy, grouping and aggregating 3D points based on increasing levels of structural complexity. This strategy aligns 3D points from less complex (e.g., ground) to more complex structures (e.g., tree trunks, foliage) sequentially, refining alignment iteratively. Empirical evidence demonstrates the method's effectiveness in aligning scans, with RMSE errors of less than 0.75 degrees in rotation and 5.5 cm in translation in the TLS to TLS case and of 0.8 degrees and 8 cm in the TLS to ALS case, respectively. These results demonstrate that ForestAlign can effectively integrate TLS-to-TLS and TLS-to-ALS forest scans, making it a valuable tool in GPS-denied areas without relying on manually placed targets, while achieving high performance. |
| title | ForestAlign: Automatic Forest Structure-based Alignment for Multi-view TLS and ALS Point Clouds |
| topic | Robotics |
| url | https://arxiv.org/abs/2302.12989 |