PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909844641939456 |
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| author | Li, Bingquan Wang, Ning Zhang, Tianwei He, Zhicheng Wu, Yucong |
| author_facet | Li, Bingquan Wang, Ning Zhang, Tianwei He, Zhicheng Wu, Yucong |
| contents | Recently, biped robot walking technology has been significantly developed, mainly in the context of a bland walking scheme. To emulate human walking, robots need to step on the positions they see in unknown spaces accurately. In this paper, we present PolyMap, a perception-based locomotion planning framework for humanoid robots to climb stairs. Our core idea is to build a real-time polygonal staircase plane semantic map, followed by a footstep planar using these polygonal plane segments. These plane segmentation and visual odometry are done by multi-sensor fusion(LiDAR, RGB-D camera and IMUs). The proposed framework is deployed on a NVIDIA Orin, which performs 20-30 Hz whole-body motion planning output. Both indoor and outdoor real-scene experiments indicate that our method is efficient and robust for humanoid robot stair climbing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12346 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing Li, Bingquan Wang, Ning Zhang, Tianwei He, Zhicheng Wu, Yucong Robotics Recently, biped robot walking technology has been significantly developed, mainly in the context of a bland walking scheme. To emulate human walking, robots need to step on the positions they see in unknown spaces accurately. In this paper, we present PolyMap, a perception-based locomotion planning framework for humanoid robots to climb stairs. Our core idea is to build a real-time polygonal staircase plane semantic map, followed by a footstep planar using these polygonal plane segments. These plane segmentation and visual odometry are done by multi-sensor fusion(LiDAR, RGB-D camera and IMUs). The proposed framework is deployed on a NVIDIA Orin, which performs 20-30 Hz whole-body motion planning output. Both indoor and outdoor real-scene experiments indicate that our method is efficient and robust for humanoid robot stair climbing. |
| title | PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.12346 |