Navigation of a Self-Driving Vehicle Using One Fiducial Marker

TL;DR

This paper introduces a vision-based navigation framework for self-driving vehicles that uses a single fiducial marker with multiple cameras and a Kalman filter to resolve rotational ambiguity, enabling accurate localization and control.

Contribution

It presents a novel multi-camera setup with a Kalman filter fusion method and an algorithm to resolve rotational ambiguity using only one planar marker.

Findings

Effective localization with a single marker demonstrated

Kalman filter fusion improves navigation accuracy

Rotational ambiguity is successfully resolved in experiments

Abstract

Navigation using only one marker, which contains four artificial features, is a challenging task since camera pose estimation using only four coplanar points suffers from the rotational ambiguity problem in a real-world application. This paper presents a framework of vision-based navigation for a self-driving vehicle equipped with multiple cameras and a wheel odometer. A multiple camera setup is presented for the camera cluster which has 360-degree vision such that our framework solely requires one planar marker. A Kalman-Filter-based fusion method is introduced for the multiple-camera and wheel odometry. Furthermore, an algorithm is proposed to resolve the rotational ambiguity problem using the prediction of the Kalman Filter as additional information. Finally, the lateral and longitudinal controllers are provided. Experiments are conducted to illustrate the effectiveness of the theory.