Online 6DoF Pose Estimation in Forests using Cross-View Factor Graph Optimisation and Deep Learned Re-localisation

Abstract-- This paper presents a novel approach for robust global localisation and 6DoF pose estimation of ground robots in forest environments by leveraging cross-view factor graph optimisation and deep-learned re-localisation. The proposed method addresses the challenges of aligning aerial and ground data for pose estimation, which is crucial for accurate pointto-point navigation in GPS-denied environments. By integrating information from both perspectives into a factor graph framework, our approach effectively estimates the robot's global position and orientation. Experimental results show that our proposed localisation system can achieve drift-free localisation with bounded positioning errors, ensuring reliable and safe robot navigation under canopies. Reliable geo-localisation in forest environments is crucial for executing various robotics tasks ranging from forest inventory and monitoring to search and rescue missions.