Research on self-supervised depth estimation algorithm of driving scene based on monocular vision

A self-supervised algorithm based on deep learning is designed to estimate the depth of the driving scene. The depth estimation network and pose estimation network designed based on convolutional neural network take the video information obtained by monocular camera as the input, and output the depth map of each frame of input image and the pose changes between two adjacent frames of input images, respectively. The view synthesis, that is, the image reconstruction loss between two adjacent frame images, is used as the supervision signal to train the neural network. The problem of scale inconsistency in monocular depth estimation is solved through the scale consistency loss, and the weight mask obtained from the scale inconsistency loss is used to solve the dynamic problems and the adverse effects of occluded objects in driving environment. The tests results show that the designed self-supervised depth estimation algorithm based on monocular video information shows high accuracy on the KITTI dataset and almost reaches the same level as the supervised algorithm.

Automated summary

A self-supervised algorithm based on deep learning is designed to estimate the depth of the driving scene. The algorithm utilizes the video information from a monocular camera to train the depth estimation network and pose estimation network. The algorithm addresses the issues of scale inconsistency and occlusion in the driving environment using view synthesis and scale consistency loss. The results show that the algorithm achieves high accuracy on the KITTI dataset.