Spherical Visual Gyroscope for Autonomous Robots Using the Mixture of Photometric Potentials

摘要

In this paper, we present a new direct omnidirectional visual gyroscope for mobile robotic platforms. The gyroscope estimates the 3D orientation of a camera-robot by comparing the current spherical image with that acquired at a reference pose. By transforming pixel intensities into a Mixture of Photometric Potentials, we introduce a novel image-similarity measure which can be seamlessly integrated into a classical nonlinear least-squares optimization scheme, offering an extended convergence domain. Our method provides accurate and robust attitude estimates, and it is easy-to-use since it involves a single tuning parameter, the width of the photometric potentials (Gaussian functions, in this work) controlling the power of attraction of each pixel. The visual gyroscope has been successfully tested on spherical image sequences generated by a twin-fisheye camera mounted on the end-effector of a robot arm and on a fixed-wing UAV.