Uploaded September 2020 | Updated September 2026, 1 week ago
As part of our outreach initiatives, our Engineers Katrina Lo Surdo, Team Leader Les Overs and John Scolaro for our sister group Distributed Sensing Systems gave an online guest lecture to second electrical, electronic, mechatronic and software engineering students for a subject which looks at microprocessors/microcontrollers and C programming.
Our continued engagement in outreach activities aim to inspire students about options in the areas of robotics, autonomous, and distributed sensing systems, which all form part of our work as the Cyber-Physical Systems Research Program.
As part of our outreach initiatives, our Engineers Katrina Lo Surdo, Team Leader Les Overs and John Scolaro for our sister group Distributed Sensing Systems gave an online guest lecture to second electrical, electronic, mechatronic and software engineering students for a subject which looks at microprocessors/microcontrollers and C programming.
Our continued engagement in outreach activities aim to inspire students about options in the areas of robotics, autonomous, and distributed sensing systems, which all form part of our work as the Cyber-Physical Systems Research Program.
![[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")
