Obstacle Detection for Mobile Robots Using Computer Vision

Computer vision is a field of computer science that has been heavily researched in recent years. Its applications in robotics are diverse ranging from face recognition to autonomous navigation. This project aims to research one of computer vision's most important contributions to the navigation of mobile robots-obstacle detection Multi-view relations are used as the fundamental principles upon which the solution is based. Planar homography, epipolar geometry and image segmentation are used to analyse the reliability of three obstacle detection methods comparing and contrasting their performance in different scenes. Pairs of images of a scene are taken by a digital camera that is moved between images. The first method uses planar homography to create a warped image from the initial image and performs obstacle detection via its comparison to the final image. The second method estimates heights of corners on the scene using planar homography and gives the heights of each distinguishable object on the scene using image segmentation. The third method uses epipolar geometry and edge detection to find point correspondences on edges between the images and then uses planar homography to compute the heights along the contours thereby performing obstacle detection.

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