부산대학교 도서관

Long-range target geolocalization in outdoor complex environments has been a long-term challenge in intelligent transportation and autonomous vehicles with great interest in fields of vehicle detection, monitoring, and security. However, since traditional monocular or binocular geolocalization methods are typically implemented by depth estimation or parallax computation, suffering from large errors when targets are far away, and thus hard to be directly applied to outdoor environments. In this paper, we propose a visual servo-based global geolocalization system, namely VSG, which takes the target position information in the binocular camera images as the control signals, automatically solves the global positions according to the gimbal rotation angles. This system solves the problem of long-range static and dynamic target geolocalization (ranging from 220 m to 1200 m), and localizes the farthest target of 1223.8 m with only 3.5$\%$ localization error. VSG also realizes full-process automation by combining the deep learning-based objection detection, and its localization performance has been proved by series of experiments. This system is the longest-range global geolocalization method with preferred accuracy reported so far, and can be deployed in different geomorphology with great robustness.