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OVPC Mesh: 3D Free-space Representation for Local Ground Vehicle Navigation

Author: Ruetz

Year: 2019

Notes:

  • sf On Visible Point Cloud Mesh, applied to rough terrain navigation, fullfil RT constraints
  • Uses the General hidden point removal operator that reduces a PC to visible points from a certain viewpoint
  • Octomap : uses an octree structure (slow map queries for insertion or lookup)
  • Voxblox : voxel hashing strategy which allocates fixed size blocks in regions of the map that contain data
  • On the contrary, this method does not rely on grid discretization
  • On fast surface reconstruction methods for large and noisy point clouds reaches near RT
  • Super slides on convex hull algorithms: https://tildesites.bowdoin.edu/~ltoma/teaching/cs3250-CompGeom/spring17/Lectures/cg-hull3d.pdf

 Method:

  • performs a radial transformation of every LiDAR point and forms a convex hull on it
  • Use the gravity aligned world frame to compute the surface angle $\alpha$
  • A polygon is traversable if $\alpha < \alpha_{max}$ and $\in [-\pi/2, \pi/2]$, anfd if the maximum height difference between the vertices is smaller than a thresh
  • Generates a point cloud from the mesh with all the vertices as a map representation for path planning

Experiments

  • Use a robot centric point cloud buffer of 20-30 scans
  • Use RRTConnect path planner
  • Simulates noisy pc => normal estimation is better using OCPV mesh
  • Three runs on rough terrain of several hectometers
  • Has a smaller and less spreaded run time than octomap and elevation mapping
  • able to handle hoverhang scenarios (roof)
  • Very sensitive to outliers in LiDAR data => voxel filter needed
  • Convex hull algorithm ensures a watertight mesh

@INPROCEEDINGS{ovpcmeshRuetz, author={Ruetz, Fabio and Hernández, Emili and Pfeiffer, Mark and Oleynikova, Helen and Cox, Mark and Lowe, Thomas and Borges, Paulo}, booktitle={2019 International Conference on Robotics and Automation (ICRA)}, title={OVPC Mesh: 3D Free-space Representation for Local Ground Vehicle Navigation}, year={2019}, volume={}, number={}, pages={8648-8654}, doi={10.1109/ICRA.2019.8793503} }