Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910722203582464 |
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| author | Jiao, Jianhao Geng, Ruoyu Li, Yuanhang Xin, Ren Yang, Bowen Wu, Jin Wang, Lujia Liu, Ming Fan, Rui Kanoulas, Dimitrios |
| author_facet | Jiao, Jianhao Geng, Ruoyu Li, Yuanhang Xin, Ren Yang, Bowen Wu, Jin Wang, Lujia Liu, Ming Fan, Rui Kanoulas, Dimitrios |
| contents | The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and semantic information consistency. In this paper, we introduce an online metric-semantic mapping system that utilizes LiDAR-Visual-Inertial sensing to generate a global metric-semantic mesh map of large-scale outdoor environments. Leveraging GPU acceleration, our mapping process achieves exceptional speed, with frame processing taking less than 7ms, regardless of scenario scale. Furthermore, we seamlessly integrate the resultant map into a real-world navigation system, enabling metric-semantic-based terrain assessment and autonomous point-to-point navigation within a campus environment. Through extensive experiments conducted on both publicly available and self-collected datasets comprising 24 sequences, we demonstrate the effectiveness of our mapping and navigation methodologies. Code has been publicly released: https://github.com/gogojjh/cobra |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00291 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments Jiao, Jianhao Geng, Ruoyu Li, Yuanhang Xin, Ren Yang, Bowen Wu, Jin Wang, Lujia Liu, Ming Fan, Rui Kanoulas, Dimitrios Robotics Computer Vision and Pattern Recognition The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and semantic information consistency. In this paper, we introduce an online metric-semantic mapping system that utilizes LiDAR-Visual-Inertial sensing to generate a global metric-semantic mesh map of large-scale outdoor environments. Leveraging GPU acceleration, our mapping process achieves exceptional speed, with frame processing taking less than 7ms, regardless of scenario scale. Furthermore, we seamlessly integrate the resultant map into a real-world navigation system, enabling metric-semantic-based terrain assessment and autonomous point-to-point navigation within a campus environment. Through extensive experiments conducted on both publicly available and self-collected datasets comprising 24 sequences, we demonstrate the effectiveness of our mapping and navigation methodologies. Code has been publicly released: https://github.com/gogojjh/cobra |
| title | Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.00291 |