Fixed-lag Sampling Strategies for Particle Filtering SLAM

Kris Beevers Wes Huang Rensselaer Polytechnic Institute Applied Perception, Inc.

NEES error for different sampling techniques

• Two new Monte Carlo sampling techniques for SLAM• Fixed-lag roughening: MCMC move step applied to trajectory• Block proposal: sample from optimal joint distribution of poses over a fixed lag• Exploits “future” information to improve past pose estimates• Results: significant consistency improvements over FastSLAM 2