Incorporating Learned Depth Perception Into Monocular Visual Odometry to Improve Scale Recovery

growing interest in autonomous driving has led to a comprehensive study of visual odometry (VO). It has been well studied how VO can estimate the pose of moving objects by examining the images taken from onboard cameras. In the last decade, it has been proposed that deep learning under supervision can be employed to estimate depth maps and visual odometry (VO). In this paper, we propose a DPT (Dense Prediction Transformer)-based monocular visual odometry method for scale estimation. Scale-drift problems are common in traditional monocular systems and in recent deep learning studies. In order to recover the scale, it is imperative that depth estimation to be accurate. A framework for dense prediction challenges that bases its computation on vision transformers instead of convolutional networks is characterized as an accurate model that is utilized to estimate depth maps. Scale recovery and depth refinement are accomplished iteratively. This allows our approach to simultaneously increase the depth estimate while eradicating scale drift. The depth map estimated using the DPT model is accurate enough for the purpose of achieving the best efficiency possible on a VO benchmark, eliminating the scaling drift issue.