Real-time Vision-based Lateral Drift Correction

Abstract

A major drawback in many robotics projects is the dependance on a specific environment and the otherwise uncertain behavior of the hardware. Simple navigation tasks like driving in a straight line can lead to a strong lateral drift over time in an unknown environment. In this paper we propose a fast and simple solution for the lateral drift problem for vision guided robots by real-time scene analysis. Without an environment-specific calibration of the robot s drive system, we balance the differential drive speed on the fly. Therefore, a feature detector is used on consecutive images. Detected feature points determine the focus of expansion (FOE) that is used for locating and correcting the robot s lateral drift. Results are presented for an unmodified real-world indoor environment that demonstrate that our method is able to correct most lateral drift, solely based on real-time vision processing.