Uploaded April 2016 | Updated September 2026, 1 week ago
CSIRO's Autonomous John Deere TE Gator is ROS enabled and ready for use. This video is an example of the vehicle and some of the sensing capabilities. This system is capable of being adapted to many different applications.
Please check the related publications:
Ralph Aeschimann and Paulo V. K. Borges “Ground or Obstacles? Detecting clear paths in vehicle navigation,” in IEEE International Conference on Robotics and Automation, Seattle, USA, May, 2015.
Robert Bichsel and Paulo V. K. Borges “Discrete-Continuous clustering for obstacle detection using stereo-vision,” IEEE International Conference on Intelligent Robots and Systems, Hamburg, Germany, September, 2015.
Paulo V. K. Borges and Peyman Moghdam ``Combining Motion and Appearance for Scene Segmentation'', in IEEE International Conference on Robotics and Automation, Hong Kong, China, May, 2014.
Stefan Hoermann and Paulo V. K. Borges ``Vehicle localization and classification using off-board vision and 3-D models,'' IEEE Transactions on Robotics, December, 2013.
Paulo V. K. Borges, Robert Zlot and Ashley Tews, ``Integrating Off-board Cameras and Vehicle On-Board Localization for Pedestrian Safety'', IEEE Transactions on Intelligent Transportation Systems, June, 2013.
Paulo V. K. Borges, Nicola Conci, Andrea Cavallaro ``Video-based human behaviour understanding: a survey,'' IEEE Transactions on Circuits and Systems for Video Technology, May, 2013.
CSIRO's Autonomous John Deere TE Gator is ROS enabled and ready for use. This video is an example of the vehicle and some of the sensing capabilities. This system is capable of being adapted to many different applications.
Please check the related publications:
Ralph Aeschimann and Paulo V. K. Borges “Ground or Obstacles? Detecting clear paths in vehicle navigation,” in IEEE International Conference on Robotics and Automation, Seattle, USA, May, 2015.
Robert Bichsel and Paulo V. K. Borges “Discrete-Continuous clustering for obstacle detection using stereo-vision,” IEEE International Conference on Intelligent Robots and Systems, Hamburg, Germany, September, 2015.
Paulo V. K. Borges and Peyman Moghdam ``Combining Motion and Appearance for Scene Segmentation'', in IEEE International Conference on Robotics and Automation, Hong Kong, China, May, 2014.
Stefan Hoermann and Paulo V. K. Borges ``Vehicle localization and classification using off-board vision and 3-D models,'' IEEE Transactions on Robotics, December, 2013.
Paulo V. K. Borges, Robert Zlot and Ashley Tews, ``Integrating Off-board Cameras and Vehicle On-Board Localization for Pedestrian Safety'', IEEE Transactions on Intelligent Transportation Systems, June, 2013.
Paulo V. K. Borges, Nicola Conci, Andrea Cavallaro ``Video-based human behaviour understanding: a survey,'' IEEE Transactions on Circuits and Systems for Video Technology, May, 2013.
![[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")
