Autonomous navigation in dense vegetation remains an open challenge and is an area of major interest for the research community. In this paper we propose a novel traversability estimation method, the Forest Traversability Map, that gives autonomous ground vehicles the ability to navigate in harsh forests or densely vegetated environments. The method estimates travers ability in unstructured environments dominated by vegetation, void of any dominant human structures, gravel or dirt roads, with higher accuracy than the state of the art: we demonstrate an improvement of over 20% F1 score (from 0.71 to 0.91) on challenging real-world data. Our method is based on 3D voxel representation and introduces a robust colour fusion method to overcome occlusion and frequent changes of lighting conditions in these environments. We also introduce and fuse multi-return lidar measurements into our probabilistic map representation in a recursive manner. Finally, we include information of neighboring voxels to increase our ability to assess the terrain travers ability correctly. These measures improve the state-of-the-art results and allow for effective traversability estimation in very challenging, densely vegetated environments.

This paper presents a novel approach for local 3D environment representation for autonomous unmanned ground vehicle (UGV) navigation called On Visible Point Clouds Mesh (OVPC Mesh). Our approach represents the surrounding of the robot as a watertight 3D mesh generated from local point cloud data in order to represent the free space surrounding the robot. It is a conservative estimation of the free space and provides a desirable trade-off between representation precision and computational efficiency, without having to discretize the environment into a fixed grid size. Our experiments analyze the usability of the approach for UGV navigation in rough terrain, both in simulation and in a fully integrated real-world system. Additionally, we compare our approach to well-known state-of the-art solutions, such as Octomap and Elevation Mapping and show that OVPC Mesh can provide reliable 3D information for trajectory planning while fulfilling real-time constraints.

A magnetic-tip steerable needle is presented with application to aiding deep brain stimulation electrode placement. The magnetic needle is 1.3mm in diameter at the tip with a 0.7mm diameter shaft, which is selected to match the size of a deep brain stimulation electrode. The tip orientation is controlled by applying torques to the embedded neodymium-iron-boron permanent magnets with a clinically-sized magnetic-manipulation system. The prototype design is capable of following trajectories under human-in-the-loop control with minimum bend radii of 100mm without inducing tissue damage and down to 30mm if some tissue damage is tolerable. The device can be retracted and redirected to reach multiple targets with a single insertion point.