We propose an automatic method for pose and motion estimation against a ground surface for a ground-moving robot-mounted monocular camera. The framework adopts a semi-dense approach that benefits from both a feature-based method and an image-registration-based method by setting multiple patches in the image for displacement computation through a highly accurate image-registration technique. To improve accuracy, we introduce virtual inverse perspective mapping (IPM) in the refinement step to eliminate the perspective effect on image registration. The pose and motion are jointly and robustly estimated by a formulation of geometric bundle adjustment via virtual IPM. Unlike conventional visual odometry methods, the proposed method is free from cumulative error because it directly estimates pose and motion against the ground by taking advantage of a camera configuration mounted on a ground-moving robot where the camera's vertical motion is ignorable compared to its height within the frame interval and the nearby ground surface is approximately flat. We conducted experiments in which the relative mean error of the pitch and roll angles was approximately 1.0 degrees and the absolute mean error of the travel distance was 0.3 mm, even under camera shaking within a short period.

翻译：我们提出了一种面向地面移动机器人单目相机的自动位姿与运动估计方法。该框架采用半稠密方法，通过图像中设置多个补丁，利用高精度图像配准技术进行位移计算，兼具基于特征的方法与基于图像配准方法的优势。为提升精度，我们在优化步骤中引入虚拟逆透视映射以消除透视效应对图像配准的影响。通过基于虚拟逆透视映射的几何光束平差，实现了对位姿与运动的联合鲁棒估计。与传统的视觉里程计方法不同，本方法充分利用安装在地面移动机器人上的相机配置特性——在帧间间隔内，相机垂直运动相对于高度可忽略不计，且近地面近似平坦——直接估计相对地面的位姿与运动，因此完全避免了累积误差。实验结果表明，即使在相机短时抖动条件下，俯仰角和滚转角的相对平均误差约为1.0度，行驶距离的绝对平均误差仅为0.3毫米。