Monocular obstacle detection for real-world environments

Abstract

In this paper, we present a feature based approach for monocular scene reconstruction based on extended Kalman filters (EKF). Our method processes a sequence of images taken by a single camera mounted in front of a mobile robot. Using various techniques we are able to produce a precise reconstruction that is almost free from outliers and therefore can be used for reliable obstacle detection and avoidance. In real-world field tests we show that the presented approach is able to detect obstacles that can not be seen by other sensors, such as laser range finders. Furthermore, we show that visual obstacle detection combined with a laser range finder can increase the detection rate of obstacles considerably, allowing the autonomous use of mobile robots in complex public and home environments.