Geometric constraints for robot navigation using omnidirectional camera

Abstract

This paper presents geometric techniques for self-localization improvement, especially for the robots equipped with a single catadioptric camera. We take the vertical line and intersection point matching into account, and proposed a novel descriptor named “Double-Gaussian vector”. The vector uses two Gaussian matrices to blur the process image region and build the corresponding feature vectors for solving the vertical line matching in two consecutive video frames. For ground plane estimation, the perpendicular lines with respect to optical axis are extracted by two approximate curve equations. The equations then crop the ground plane area of the omnidirectional image. The sparse bundle adjustment (SBA) is adopted for iterative calculating the 3D matching points between two robot locations for optimizing the robot pose estimation. The convergent 3D points are used to compute the robot poses and record the navigation trajectory. The results show that the proposed methods significantly improve the robot localization and navigation compared to the previous literature in the experiments.