Uploaded July 2014 | Updated September 2026, 1 week ago
Legged robots such as hexapod robots are capable of navigating in rough and unstructured terrain. When the terrain model is either known a priori or is observed by on-board sensors, motion planners can be used to give desired motion and stability for the robot. However, unexpected leg disturbances could occur due to inaccuracies of the model or sensors or simply due to the dynamic nature of the terrain. We provide a state space based framework for stabilisation of a high dimensional multi-legged robot which detects and recovers from unexpected events such as leg slip. We experimentally evaluate our approach using a modified PhantomX hexapod robot with extended tibia segments which significantly reduces its stability. Our results show that roll and pitch stability is improved by 2x when using the proposed method.
For more details, please see full paper: navinda.kottege.net/publications/hoerger2014.ISER14.pdf
Legged robots such as hexapod robots are capable of navigating in rough and unstructured terrain. When the terrain model is either known a priori or is observed by on-board sensors, motion planners can be used to give desired motion and stability for the robot. However, unexpected leg disturbances could occur due to inaccuracies of the model or sensors or simply due to the dynamic nature of the terrain. We provide a state space based framework for stabilisation of a high dimensional multi-legged robot which detects and recovers from unexpected events such as leg slip. We experimentally evaluate our approach using a modified PhantomX hexapod robot with extended tibia segments which significantly reduces its stability. Our results show that roll and pitch stability is improved by 2x when using the proposed method.
For more details, please see full paper: navinda.kottege.net/publications/hoerger2014.ISER14.pdf
![[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")
