Determining Location and Detecting Changes Using a Single Training Video

Abstract

This paper proposes a new approach to find a robot’s current location and to detect any changes in its path, using monocular vision. A recorded single obstacle-free training video is first obtained and saved. Then, a moving robot can use its camera to find its current location, within its path, by matching current frames with the ones from the training video. This frame-to-frame matching is performed using extracted feature points. Once a match is found, the corresponding frames are aligned (registered) using a homography that is calculated based the matched feature points. This allows to compensate for viewpoint changes between the observed and saved frames. Finally, we compare the regions of interest (ROIs) of the aligned frames, using their colour histograms. We carried out seventeen tests using this approach. The videos, for both training and testing, were recorded using off-the-shelf phone camera by walking down different paths. Four tests were performed in an outdoor environment, and 13 in an indoor environment. Our tests have shown excellent results, with an accuracy above 95% for most of them, for finding the robot’s location and for detecting obstacles in the robot’s path. Both training and testing videos used in our tests were realistic and very challenging, as they consisted of a mix of indoor and outdoor environments with cluttered backgrounds, repetitive floor textures and glare.