BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929615525642240 |
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| author | Torres, Miguel Arturo Vega Ribic, Anna de Soto, Borja García Borrmann, André |
| author_facet | Torres, Miguel Arturo Vega Ribic, Anna de Soto, Borja García Borrmann, André |
| contents | This paper introduces BIMCaP, a novel method to integrate mobile 3D sparse LiDAR data and camera measurements with pre-existing building information models (BIMs), enhancing fast and accurate indoor mapping with affordable sensors. BIMCaP refines sensor poses by leveraging a 3D BIM and employing a bundle adjustment technique to align real-world measurements with the model. Experiments using real-world open-access data show that BIMCaP achieves superior accuracy, reducing translational error by over 4 cm compared to current state-of-the-art methods. This advancement enhances the accuracy and cost-effectiveness of 3D mapping methodologies like SLAM. BIMCaP's improvements benefit various fields, including construction site management and emergency response, by providing up-to-date, aligned digital maps for better decision-making and productivity. Link to the repository: https://github.com/MigVega/BIMCaP |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03434 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement Torres, Miguel Arturo Vega Ribic, Anna de Soto, Borja García Borrmann, André Robotics Artificial Intelligence This paper introduces BIMCaP, a novel method to integrate mobile 3D sparse LiDAR data and camera measurements with pre-existing building information models (BIMs), enhancing fast and accurate indoor mapping with affordable sensors. BIMCaP refines sensor poses by leveraging a 3D BIM and employing a bundle adjustment technique to align real-world measurements with the model. Experiments using real-world open-access data show that BIMCaP achieves superior accuracy, reducing translational error by over 4 cm compared to current state-of-the-art methods. This advancement enhances the accuracy and cost-effectiveness of 3D mapping methodologies like SLAM. BIMCaP's improvements benefit various fields, including construction site management and emergency response, by providing up-to-date, aligned digital maps for better decision-making and productivity. Link to the repository: https://github.com/MigVega/BIMCaP |
| title | BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03434 |