用单目相机实现攀爬机器人3D地形建图，兼顾轻量低功耗与精度。
3D Mapping Using a Lightweight and Low-Power Monocular Camera Embedded inside a Gripper of Limbed Climbing Robots
- 融合单目视觉与机械臂运动学信息，解决单目定位尺度模糊问题。
- 实现实时度量尺度的3D地图构建，支持凸面地形自主抓取。
- 适合空间探测中对重量和能耗敏感的攀爬机器人系统。
腿式攀爬机器人被设计用于探索如月球和火星穹顶等复杂垂直墙面。在这些机器人中，手眼相机的主要作用是通过近距离视角准确估计可抓握点（即凸地形表面）的三维位置。传统攀爬机器人常采用体积大、功耗高的RGB-D相机作为手眼相机以实现直接的3D地形建图与可抓点检测。本文提出一种面向太空探测的3D地形建图系统，适用于配备单目手眼相机的腿式攀爬机器人。相比RGB-D相机，单目相机结构更轻巧紧凑、功耗更低。尽管单目SLAM可构建3D地图，但存在尺度模糊问题。为此，我们提出一种融合单目视觉约束与机械臂正向运动学的新SLAM方法，通过因子图优化联合估计时序抓取器位姿与全局度量尺度。实验基于物理仿真与真实场景验证，结果表明该框架能在实时条件下构建度量尺度的3D地形地图，并实现基于单目相机的凸地形表面自主抓取，无需依赖RGB-D相机。本方法为未来涉及腿式攀爬机器人的深空任务提供了可扩展且节能的感知方案。
原文摘要 · Abstract (English)
Limbed climbing robots are designed to explore challenging vertical walls, such as the skylights of the Moon and Mars. In such robots, the primary role of a hand-eye camera is to accurately estimate 3D positions of graspable points (i.e., convex terrain surfaces) thanks to its close-up views. While conventional climbing robots often employ RGB-D cameras as hand-eye cameras to facilitate straightforward 3D terrain mapping and graspable point detection, RGB-D cameras are large and consume considerable power. This work presents a 3D terrain mapping system designed for space exploration using limbed climbing robots equipped with a monocular hand-eye camera. Compared to RGB-D cameras, monocular cameras are more lightweight, compact structures, and have lower power consumption. Although monocular SLAM can be used to construct 3D maps, it suffers from scale ambiguity. To address this limitation, we propose a SLAM method that fuses monocular visual constraints with limb forward kinematics. The proposed method jointly estimates time-series gripper poses and the global metric scale of the 3D map based on factor graph optimization. We validate the proposed framework through both physics-based simulations and real-world experiments. The results demonstrate that our framework constructs a metrically scaled 3D terrain map in real-time and enables autonomous grasping of convex terrain surfaces using a monocular hand-eye camera, without relying on RGB-D cameras. Our method contributes to scalable and energy-efficient perception for future space missions involving limbed climbing robots. See the video summary here: https://youtu.be/fMBrrVNKJfc
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