SoMaSLAM: 2D Graph SLAM for Sparse Range Sensing with Soft Manhattan World Constraints
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913514268917760 |
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| author | Han, Jeahn Hu, Zichao Yang, Seonmo Kim, Minji Kim, Pyojin |
| author_facet | Han, Jeahn Hu, Zichao Yang, Seonmo Kim, Minji Kim, Pyojin |
| contents | We propose a graph SLAM algorithm for sparse range sensing that incorporates a soft Manhattan world utilizing landmark-landmark constraints. Sparse range sensing is necessary for tiny robots that do not have the luxury of using heavy and expensive sensors. Existing SLAM methods dealing with sparse range sensing lack accuracy and accumulate drift error over time due to limited access to data points. Algorithms that cover this flaw using structural regularities, such as the Manhattan world (MW), have shortcomings when mapping real-world environments that do not coincide with the rules. We propose SoMaSLAM, a 2D graph SLAM designed for tiny robots with sparse range sensing. Our approach effectively maps sparse range data without enforcing strict structural regularities and maintains an adaptive graph. We implement the MW assumption as soft constraints, which we refer to as a soft Manhattan world. We propose novel soft landmark-landmark constraints to incorporate the soft MW into graph SLAM. Through extensive evaluation, we demonstrate that our proposed SoMaSLAM method improves localization accuracy on diverse datasets and is flexible enough to be used in the real world. We release our source code and sparse range datasets at https://SoMaSLAM.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15736 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SoMaSLAM: 2D Graph SLAM for Sparse Range Sensing with Soft Manhattan World Constraints Han, Jeahn Hu, Zichao Yang, Seonmo Kim, Minji Kim, Pyojin Robotics We propose a graph SLAM algorithm for sparse range sensing that incorporates a soft Manhattan world utilizing landmark-landmark constraints. Sparse range sensing is necessary for tiny robots that do not have the luxury of using heavy and expensive sensors. Existing SLAM methods dealing with sparse range sensing lack accuracy and accumulate drift error over time due to limited access to data points. Algorithms that cover this flaw using structural regularities, such as the Manhattan world (MW), have shortcomings when mapping real-world environments that do not coincide with the rules. We propose SoMaSLAM, a 2D graph SLAM designed for tiny robots with sparse range sensing. Our approach effectively maps sparse range data without enforcing strict structural regularities and maintains an adaptive graph. We implement the MW assumption as soft constraints, which we refer to as a soft Manhattan world. We propose novel soft landmark-landmark constraints to incorporate the soft MW into graph SLAM. Through extensive evaluation, we demonstrate that our proposed SoMaSLAM method improves localization accuracy on diverse datasets and is flexible enough to be used in the real world. We release our source code and sparse range datasets at https://SoMaSLAM.github.io/. |
| title | SoMaSLAM: 2D Graph SLAM for Sparse Range Sensing with Soft Manhattan World Constraints |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.15736 |