Uploaded August 2020 | Updated September 2026, 1 week ago
A video of PaintCloud and Wildcat SLAM from a ground vehicle at QCAT.
For more information check: https://research.csiro.au/robotics/paintcloud-colourising-3d-representations-of-the-world-based-on-lidar-data/
A video of PaintCloud and Wildcat SLAM from a ground vehicle at QCAT.
For more information check: https://research.csiro.au/robotics/paintcloud-colourising-3d-representations-of-the-world-based-on-lidar-data/
![[IROS 2025] FGLoc6D: Online 6DoF Cross-View Localisation in Forests](https://i.ytimg.com/vi/myCJVEPGGAA/mqdefault.jpg "[IROS 2025] FGLoc6D: Online 6DoF Cross-View Localisation in Forests
Video introducing our paper Online 6DoF Global Localisation in Forests using Semantically-Guided Re-Localisation and Cross-View Factor-Graph Optimisation, accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025).
This work 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.
Paper: https://arxiv.org/abs/2409.16680")
