GEERS: Georeferenced Enhanced EKF Using Point Cloud Registration and Segmentation

Georeferenced Enhanced EKF using point cloud Registration and Segmentation (GEERS) is a high-accuracy and consistent-rate localization method for outdoor robots. The localization is estimated by an EKF that fuses wheel odometry, IMU and GNSS measurements, in addition to feedback correc- tions from a registration step. The method improves localization accuracy by registering range sensors with pre-obtained geo- referenced 3D maps and providing feedback corrections to the EKF. The continuous fusion of GNSS measurements naturally provides an initial estimate and reduces kidnapped robot sit- uations in symmetric environments. The proposed method can integrate any range sensor (such as RBG-D cameras or 2D and 3D LiDAR). Experimental results in a real-world solar farm, its simulated digital twin, and an open dataset demonstrate localization accuracy improvements. Real-world experiments on a solar farm demonstrated the flexibility and reliability of the proposed method, exposing its advantages towards GNSS-only- based approaches.