Monocular vision-based navigation is a considerable ability for a home mobile robot. However, due to diverse disturbances, helping robots avoid obstacles, especially non-Manhattan obstacles, remains a big challenge. In indoor environments, there are many spatial right-corners that are projected into two dimensional projections with special geometric configurations. These projections, which consist of three lines, might enable us to estimate their position and orientation in 3D scenes. In this paper, we present a method for home robots to avoid non-Manhattan obstacles in indoor environments from a monocular camera. The approach first detects non-Manhattan obstacles. Through analyzing geometric features and constraints, it is possible to estimate posture differences between orientation of the robot and non-Manhattan obstacles. Finally according to the convergence of posture differences, the robot can adjust its orientation to keep pace with the pose of detected non-Manhattan obstacles, making it possible avoid these obstacles by itself. Based on geometric inferences, the proposed approach requires no prior training or any knowledge of the camera ʼ s internal parameters, making it practical for robots navigation. Furthermore, the method is robust to errors in calibration and image noise. We compared the errors from corners of estimated non-Manhattan obstacles against the ground truth. Furthermore, we evaluate the validity of convergence of differences between the robot orientation and the posture of non-Manhattan obstacles. The experimental results showed that our method is capable of avoiding non-Manhattan obstacles, meeting the requirements for indoor robot navigation.

中文翻译：

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基于室内环境中空间角的投影避免非曼哈顿障碍物

对于家庭移动机器人，基于单眼视觉的导航是一项相当大的功能。然而，由于各种干扰，帮助机器人避开障碍物，尤其是非曼哈顿障碍物，仍然是一个巨大的挑战。在室内环境中，有许多空间右角被投影为具有特殊几何构造的二维投影。这些由三行组成的投影可能使我们能够估计它们在3D场景中的位置和方向。在本文中，我们提出了一种家用机器人通过单眼相机避免在室内环境中使用非曼哈顿障碍物的方法。该方法首先检测非曼哈顿障碍。通过分析几何特征和约束，可以估计机器人的方向与非曼哈顿障碍物之间的姿态差异。最后，根据姿势差异的收敛性，机器人可以调整其方向以与检测到的非曼哈顿障碍物的姿势保持一致，从而有可能自己避免这些障碍。基于几何推论，所提出的方法不需要事先培训或对相机内部参数的任何了解，因此对于机器人导航非常实用。此外，该方法对于校准和图像噪声中的误差是鲁棒的。我们比较了估计的非曼哈顿障碍物对地面真相的错误。此外，我们评估了机器人方向与非曼哈顿障碍物姿势之间差异收敛的有效性。实验结果表明，该方法能够避免非曼哈顿障碍物，满足室内机器人导航的要求。