Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916387751985152 |
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| author | Chen, Zhiqiang Qi, Yuhua Feng, Dapeng Zhuang, Xuebin Chen, Hongbo Hu, Xiangcheng Wu, Jin Peng, Kelin Lu, Peng |
| author_facet | Chen, Zhiqiang Qi, Yuhua Feng, Dapeng Zhuang, Xuebin Chen, Hongbo Hu, Xiangcheng Wu, Jin Peng, Kelin Lu, Peng |
| contents | The ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometrically degenerate environments, limiting the development and benchmarking of robust LiDAR SLAM algorithms. To address this gap, we introduce GEODE, a comprehensive multi-LiDAR, multi-scenario dataset specifically designed to include real-world geometrically degenerate environments. GEODE comprises 64 trajectories spanning over 64 kilometers across seven diverse settings with varying degrees of degeneracy. The data was meticulously collected to promote the development of versatile algorithms by incorporating various LiDAR sensors, stereo cameras, IMUs, and diverse motion conditions. We evaluate state-of-the-art SLAM approaches using the GEODE dataset to highlight current limitations in LiDAR SLAM techniques. This extensive dataset will be publicly available at https://geode.github.io, supporting further advancements in LiDAR-based SLAM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios Chen, Zhiqiang Qi, Yuhua Feng, Dapeng Zhuang, Xuebin Chen, Hongbo Hu, Xiangcheng Wu, Jin Peng, Kelin Lu, Peng Robotics The ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometrically degenerate environments, limiting the development and benchmarking of robust LiDAR SLAM algorithms. To address this gap, we introduce GEODE, a comprehensive multi-LiDAR, multi-scenario dataset specifically designed to include real-world geometrically degenerate environments. GEODE comprises 64 trajectories spanning over 64 kilometers across seven diverse settings with varying degrees of degeneracy. The data was meticulously collected to promote the development of versatile algorithms by incorporating various LiDAR sensors, stereo cameras, IMUs, and diverse motion conditions. We evaluate state-of-the-art SLAM approaches using the GEODE dataset to highlight current limitations in LiDAR SLAM techniques. This extensive dataset will be publicly available at https://geode.github.io, supporting further advancements in LiDAR-based SLAM. |
| title | Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.04961 |