Camera-based distance estimation for autonomous vehicles

Abstract

The aim of this work is the investigation of camera-based techniques for distance estimation between two autonomous vehicles. While both monocular- and stereo-camera methods are explored, this study focuses on the usage of fiducial markers. Therefore, existing fiducial markers are discussed and selected. Based on this selection, three configurations of markers are proposed and applied to different distance estimation methods. The chosen markers are AprilTag and WhyCon. Their distances are estimated by means of Perspective-n-Point, 3D position calculation of a circle and stereo-based triangulation. Within this study the presented methods are evaluated based on their distance estimation accuracy and applicable range. They are compared with each other and with the common stereo method Semi-Global-Matching. Moreover, the influence of uncertainties is explored with reference to geometrical calibration. A setup is presented to evaluate the techniques based on real-world and simulated data. In order to gain insights on the methods properties, a simulation is used that facilitates variation of the image data. In addition, a Monte-Carlo-Simulation allows to model calibration uncertainty. The obtained observations are substantiated based on two real-world experiments. The results demonstrate the potential of fiducial markers for relative distance estimation of vehicles in terms of high accuracy and low uncertainty. The lower sensitivity to uncertainties in camera calibration makes fiducial markers preferable to stereo methods.