Case Study of Deep Learning Methods for Depth Estimation in Indoor Ground Robotics

Depth estimation is the computer vision task that assigns a distance between the camera and each pixel in an image. This paper focuses on monocular metric depth estimation in videos, which infers a distance in metric units using a single RGB camera. Considering its applications, robotics systems and environmental mapping arise as practical areas that can make extensive usage of these techniques. As a case study for indoor robotics, the ICL ground robot dataset obtained by video footage in graphic simulation was used for experiments. A comparison was made considering the results and requirements of data acquisition needed for different deep learning models, presenting self-supervised and supervised methods available in literature and being the first work to present a depth estimation benchmark for the chosen dataset.