In this paper, a navigation and environment mapping method is presented for small exploration robots that use hopping motion. While previous research about hopping rovers mostly focuses on mobility and mechanical design, the motivation for the proposed method is to provide a fully autonomous navigation system using only a monocular camera. The method accurately estimates the hopping distance and reconstruct the 3D environment using Structure from Motion, proving that a monocular system is not only feasible, but accurate and robust at the same time. The relative scale problem of the reconstructed scene and trajectory is solved by the known gravity and parabolic motion constraints. After each hop, the error in landing position is corrected by a modified Iterative Closest Point algorithm with non-overlapping part elimination. The environmental point cloud is projected onto a 2D image, that is used to find the most suitable landing position for the next hop using protrusion based obstacle detection, and navigate the robot towards the goal direction. Both virtual environment simulations and real experiments confirm the feasibility and highlight the advantages of the presented method.

中文翻译：

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单眼相机跳跃机器人导航的轨迹估计与位置校正

本文提出了一种导航和环境映射方法，用于使用跳跃运动的小型勘探机器人。尽管以前有关跳车的研究主要集中在机动性和机械设计上，但提出的方法的动机是提供仅使用单眼相机的全自动导航系统。该方法可准确估算跳跃距离并使用Motion中的Structure重建3D环境，从而证明单目系统不仅可行，而且同时准确且健壮。通过已知的重力和抛物线运动约束解决了重建场景和轨迹的相对比例问题。每跳一跳后，通过改进的迭代最近点算法（消除了不重叠的零件）来校正着陆位置的误差。将环境点云投影到2D图像上，该图像用于使用基于突起的障碍物检测为下一跳找到最合适的着陆位置，并朝着目标方向导航机器人。虚拟环境仿真和实际实验都证实了可行性并突出了所提出方法的优势。