![[IROS 2025] FGLoc6D: Online 6DoF Cross-View Localisation in Forests @AutonomousSystemsLab](https://i.ytimg.com/vi/myCJVEPGGAA/maxresdefault.jpg)
Uploaded July 2025 | Updated September 2026, 1 week ago
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: arxiv.org/abs/2409.16680
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: arxiv.org/abs/2409.16680
![[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")
![[RA-L/ICRA 2018] Complementary Perception for Handheld SLAM](https://i.ytimg.com/vi/q8NAsqOH2C0/mqdefault.jpg "[RA-L/ICRA 2018] Complementary Perception for Handheld SLAM
We present a novel method for mapping general 3D environments, where sufficient geometric or visual information is not everywhere guaranteed and where the device motion is unconstrained as with handheld systems. The continuous-time SLAM algorithm integrates a lidar, camera and inertial measurement unit in a complementary fashion whereby all sensors contribute constraints to the optimization. The proposed algorithm is designed to expand the domain of mappable environments and therefore increase the reliability and utility of general purpose mobile mapping. A key component of the proposed algorithm is the incorporation of depth uncertainty into visual features, which is effective for noisy surfaces and allows features with and without depth estimates to be modeled in a unified manner. Results demonstrate a wider mappable domain on challenging environments compared to state-of-the-art lidar or vision based localization and mapping algorithms.")
