Research on Outdoor Localization of Robot Integrating Multiple Information Sources

Abstract

In the field of robotics, outdoor localization technology has always been a research focus. Localization systems collect spatial orientation data through sensors and use built-in algorithms to calculate and determine the precise location of the robot. However, the complexity of outdoor environments, such as significant changes in lighting, variable weather conditions, and the vast range of localization, pose severe challenges to outdoor localization technology. To address this issue, research on multi-information fusion for robot outdoor localization has provided the possibility for achieving high-precision and high-robustness localization, and its application value in the field of outdoor robots is extremely significant. This study proposes an outdoor localization method based on the fusion of images, odometry, inertial navigation systems, and differential global positioning systems (DGPS). This method optimizes the localization scheme in situations where GPS signal quality is poor or lost, thus achieving precise localization of targets in various complex outdoor environments.