A simultaneous localization and map-building method for lunar rover based on particle filter

For an accurate position and orientation estimation of Lunar Rover, a method of SLAM based on Rao-Blackwellised Particle Filter is introduced in this article. In this approach environment measurements obtained by high speed Laser Range Finder is aligned with the constructed grid map. As the position estimation by SLAM based on particle filter is hard to eliminate the error accumulated in traveling, we proposed an estimation method for translation velocity of Lunar Rover, which is validated by simulation experiments. To improve real-time performance, we propose that all the particles maintain a common map. Simulation experiment reveals that this map management method does not degenerate filter effect while dramatically enhancing its real-time performance.