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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/2309.10900 |
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| _version_ | 1866917642154016768 |
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| author | Goel, Kshitij Tabib, Wennie |
| author_facet | Goel, Kshitij Tabib, Wennie |
| contents | This letter describes an incremental multimodal surface mapping methodology, which represents the environment as a continuous probabilistic model. This model enables high-resolution reconstruction while simultaneously compressing spatial and intensity point cloud data. The strategy employed in this work utilizes Gaussian mixture models (GMMs) to represent the environment. While prior GMM-based mapping works have developed methodologies to determine the number of mixture components using information-theoretic techniques, these approaches either operate on individual sensor observations, making them unsuitable for incremental mapping, or are not real-time viable, especially for applications where high-fidelity modeling is required. To bridge this gap, this letter introduces a spatial hash map for rapid GMM submap extraction combined with an approach to determine relevant and redundant data in a point cloud. These contributions increase computational speed by an order of magnitude compared to state-of-the-art incremental GMM-based mapping. In addition, the proposed approach yields a superior tradeoff in map accuracy and size when compared to state-of-the-art mapping methodologies (both GMM- and not GMM-based). Evaluations are conducted using both simulated and real-world data. The software is released open-source to benefit the robotics community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_10900 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models Goel, Kshitij Tabib, Wennie Robotics Computer Vision and Pattern Recognition This letter describes an incremental multimodal surface mapping methodology, which represents the environment as a continuous probabilistic model. This model enables high-resolution reconstruction while simultaneously compressing spatial and intensity point cloud data. The strategy employed in this work utilizes Gaussian mixture models (GMMs) to represent the environment. While prior GMM-based mapping works have developed methodologies to determine the number of mixture components using information-theoretic techniques, these approaches either operate on individual sensor observations, making them unsuitable for incremental mapping, or are not real-time viable, especially for applications where high-fidelity modeling is required. To bridge this gap, this letter introduces a spatial hash map for rapid GMM submap extraction combined with an approach to determine relevant and redundant data in a point cloud. These contributions increase computational speed by an order of magnitude compared to state-of-the-art incremental GMM-based mapping. In addition, the proposed approach yields a superior tradeoff in map accuracy and size when compared to state-of-the-art mapping methodologies (both GMM- and not GMM-based). Evaluations are conducted using both simulated and real-world data. The software is released open-source to benefit the robotics community. |
| title | Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.10900 |