This paper introduces pixel-wise prediction based visual odometry (PWVO),
which is a dense prediction task that evaluates the values of translation and
rotation for every pixel in its input observations. PWVO employs uncertainty
estimation to identify the noisy regions in the input observations, and adopts
a selection mechanism to integrate pixel-wise predictions based on the
estimated uncertainty maps to derive the final translation and rotation. In
order to train PWVO in a comprehensive fashion, we further develop a data
generation workflow for generating synthetic training data. The experimental
results show that PWVO is able to deliver favorable results. In addition, our
analyses validate the effectiveness of the designs adopted in PWVO, and
demonstrate that the uncertainty maps estimated by PWVO is capable of capturing
the noises in its input observations.

本文介绍了基于像素预测的视觉里程表（PWVO）的方法，该方法通过不确定性估计识别输入观测中的噪声区域，并采用选择机制来集成基于估计的不确定性地图的像素级预测以确定最终的平移和旋转值。通过合成训练数据的数据生成工作流，全面培训 PWVO，并且实验结果表明，PWVO 能够提供良好的结果，且估计的不确定性地图能够捕获输入观测中的噪声。