Wang Luping, Wei Hui
IEEE Trans Image Process. 2020 Sep 30;PP. doi: 10.1109/TIP.2020.3026628.
Helping mobile robots understand curved corridor scenes has considerable value in computer vision. However, due to the diversity of curved corridor scenes, such as curved structures that do not satisfy Manhattan assumption, understanding them remains a challenge. Curved non-Manhattan structures can be seen as compositions of spatial right angles projected into two dimensional projections, which may help us estimate their original posture in 3D scenes. In this paper, we presented an approach for mobile robots to understand curved corridor scenes including Manhattan and curved non-Manhattan structures, from a single image. Angle projections can be assigned to different clusters via geometric inference. Then coplanar structures can be estimated. Fold structures consisting of coplanar structures can be estimated, and curved non-Manhattan structures can be approximately represented by fold structures. Based on understanding curved non-Manhattan structures, the method is practical and efficient for a navigating mobile robot in curved corridor scenes. The algorithm requires no prior training or knowledge of the camera's internal parameters. With geometric features from a monocular camera, the method is robust to calibration errors and image noise. We compared the estimated curved layout against the ground truth and measured the percentage of pixels that were incorrectly classified. The experimental results showed that the algorithm can successfully understand curved corridor scenes including both Manhattan and curved non-Manhattan structures, meeting the requirements of robot navigation in a curved corridor environment.
帮助移动机器人理解弯曲走廊场景在计算机视觉中具有重要价值。然而，由于弯曲走廊场景的多样性，例如不满足曼哈顿假设的弯曲结构，理解这些场景仍然是一项挑战。弯曲的非曼哈顿结构可以看作是投影到二维投影中的空间直角的组合，这可能有助于我们估计它们在三维场景中的原始姿态。在本文中，我们提出了一种方法，使移动机器人能够从单张图像中理解包括曼哈顿结构和弯曲非曼哈顿结构的弯曲走廊场景。通过几何推理可以将角度投影分配到不同的簇中。然后可以估计共面结构。由共面结构组成的折叠结构可以被估计，并且弯曲的非曼哈顿结构可以用折叠结构近似表示。基于对弯曲非曼哈顿结构的理解，该方法对于在弯曲走廊场景中导航的移动机器人来说既实用又高效。该算法不需要事先训练或了解相机的内部参数。利用单目相机的几何特征，该方法对校准误差和图像噪声具有鲁棒性。我们将估计的弯曲布局与地面真值进行比较，并测量错误分类像素的百分比。实验结果表明，该算法能够成功理解包括曼哈顿结构和弯曲非曼哈顿结构的弯曲走廊场景，满足机器人在弯曲走廊环境中导航的要求。
