Autonomous navigation and localization is the foundation of unmanned system intelligence,so continuous,stable,and reliable position service in unknown environment is especially important for autonomous navigation and localization.Aiming at the problem that GNSS cannot continuously localize in complex environments due to weak signals,poor penetration ability,and susceptibility to interference;and that visual navigation and localization is only relative,this paper proposes a GNSS-assisted visual autonomous localization method,which 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 is obtained through Horn coordinate transformation,and the relative position of the subsequent image sequences in the world coordinate system is obtained through epipolar geometry constraint,homography matrix transformation,and 2D-3D position and orientation solving,which ultimately yields the global position data of unmanned carriers in the GNSS coordinate system when GNSS is temporarily unavailable.The dataset validation show that the algorithm can be applied to the case of intermittent presence of GNSS signals,and can provide global location services with high short-time positioning accuracy.Then,we further compare the traditional state estimation and BA post-optimisation error,and the results show that compared with the traditional state estimation,the BA post-optimisation improves the localisation accuracy by 10%-20%,which proves that the algorithm has good reliability and applicability in unknown environments.
关键词
视觉定位/GNSS辅助定位/BA优化/Horn坐标变换
Key words
visual navigation and positioning/GNSS assisted positioning/BA optimization/horn coordinate transformation
维普
万方数据