GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments

Abstract

Autonomous navigation and localization are the foundations of unmanned intelligent systems, therefore, continuous, stable, and reliable position services in unknown environments are especially important for autonomous navigation and localization. Aiming at the problem where GNSS cannot continuously localize in complex environments due to weak signals, poor penetration ability, and susceptibility to interference and that visual navigation and localization are only relative, this paper proposes a GNSS-aided visual dynamic localization method that can provide global localization services in unknown environments. Taking the three frames of images and their corresponding GNSS coordinates as the constraint data, the GNSS coordinate system and world coordinate system transformation matrix are obtained through horn coordinate transformation, and the relative positions of the subsequent image sequences in the world coordinate system are obtained through epipolar geometry constraints, homography matrix transformations, and 2D–3D position and orientation solving, which ultimately yields the global position data of unmanned carriers in GNSS coordinate systems when GNSS is temporarily unavailable. Both the dataset validation and measured data validation showed that the GNSS initial-assisted positioning algorithm could be applied to situations where intermittent GNSS signals exist, and it can provide global positioning coordinates with high positioning accuracy in a short period of time; however, the algorithm would drift when used for a long period of time. We further compared the errors of the GNSS initial-assisted positioning and GNSS continuous-assisted positioning systems, and the results showed that the accuracy of the GNSS continuous-assisted positioning system was two to three times better than that of the GNSS initial-assisted positioning system, which proved that the GNSS continuous-assisted positioning algorithm could maintain positioning accuracy for a long time and it had good reliability and applicability in unknown environments.