Real-time Pose Estimation on Elevation Maps for Wheeled Vehicles

Abstract

Fast and accurate obstacle detection is a crucial component for autonomous robot navigation. It becomes even more important for a shared control vehicle like an electric wheeled walker, since the safety of the vehicle and the user depend on the correct classification of obstacles. This work describes a method for pose estimation of four-wheeled vehicles, which utilizes the fixed resolution of digital elevation maps to generate a detailed vehicle model. The vehicle’s wheels are also approximated using digital elevation maps, allowing efficient calculation of wheel to ground contact points and therefore fast and accurate estimation of valid vehicle poses. To evaluate the proposed method, pose estimates are compared to three datasets including ground truth poses: one created using an external tracking system and two created by simulations of wheeled robots. It is also shown that the method is fast enough for real time operation.

BibTeX

@inproceedings{JordanIROS2017,
  title = {Real-time Pose Estimation on Elevation Maps for Wheeled Vehicles},
  author = {Jordan, Julian and Zell, Andreas},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  address = {Vancouver, Canada},
  pages = {},
  year = {2017},
  month = sep,
  abstract = {Fast and accurate obstacle detection is a crucial
component for autonomous robot navigation. It becomes even
more important for a shared control vehicle like an electric
wheeled walker, since the safety of the vehicle and the user
depend on the correct classification of obstacles. This work
describes a method for pose estimation of four-wheeled vehicles,
which utilizes the fixed resolution of digital elevation maps to
generate a detailed vehicle model. The vehicle’s wheels are also
approximated using digital elevation maps, allowing efficient
calculation of wheel to ground contact points and therefore
fast and accurate estimation of valid vehicle poses. To evaluate
the proposed method, pose estimates are compared to three
datasets including ground truth poses: one created using an
external tracking system and two created by simulations of
wheeled robots. It is also shown that the method is fast enough
for real time operation.},
  days = {24-28},
  note = {},
}