Online 6DoF Global Localisation in Forests using Semantically-Guided Re-Localisation and Cross-View Factor-Graph Optimisation

TL;DR

This paper introduces FGLoc6D, a novel method for robust online 6DoF global localisation of ground robots in forests, combining semantic re-localisation and cross-view factor graph optimisation to improve accuracy and robustness in GPS-degraded environments.

Contribution

The paper proposes a new approach integrating semantic-guided re-localisation with cross-view factor graph optimisation for forest robot localisation, enhancing keypoint repeatability and global pose accuracy.

Findings

Achieves drift-free localisation with bounded errors in dense forests.

Outperforms state-of-the-art methods in accuracy and robustness.

Demonstrates reliable navigation in GPS-degraded environments.

Abstract

This paper presents FGLoc6D, a novel approach for robust global localisation and online 6DoF pose estimation of ground robots in forest environments by leveraging deep semantically-guided re-localisation and cross-view factor graph optimisation. The proposed method addresses the challenges of aligning aerial and ground data for pose estimation, which is crucial for accurate point-to-point navigation in GPS-degraded environments. By integrating information from both perspectives into a factor graph framework, our approach effectively estimates the robot's global position and orientation. Additionally, we enhance the repeatability of deep-learned keypoints for metric localisation in forests by incorporating a semantically-guided regression loss. This loss encourages greater attention to wooden structures, e.g., tree trunks, which serve as stable and distinguishable features, thereby improving the consistency of keypoints and increasing the success rate of global registration, a process we refer to as re-localisation. The re-localisation module along with the factor-graph structure, populated by odometry and ground-to-aerial factors over time, allows global localisation under dense canopies. We validate the performance of our method through extensive experiments in three forest scenarios, demonstrating its global localisation capability and superiority over alternative state-of-the-art in terms of accuracy and robustness in these challenging environments. Experimental results show that our proposed method can achieve drift-free localisation with bounded positioning errors, ensuring reliable and safe robot navigation through dense forests.