Efficient planar features matching for robot localization using GPU

Matching image features between an image and a map
of landmarks is usually a time consuming process in mobile
robot localization or Simultaneous Localisation And Mapping
algorithms. The main problem is being able to match
features in spite of viewpoint changes. Methods based on
interest point descriptors such as SIFT have been implemented
on GPUs to reach real time performance. In this
paper, we present another way to match features with the
use of a local 3D model of the features and a motion model
of the robot. This matching algorithm dedicated to robot
localization would be much too slow if executed on a CPU.
Thanks to a GPU implementation, we show that it is possible
to achieve real-time performance while offering more
robustness than descriptor based methods.