Particle filter — Scan matching hybrid SLAM employing topological information

Two of the most predominant approaches regarding the SLAM problem are the Rao-Blackwellized particle filters and the Scan Matching algorithms, each approach presenting its own deficiencies. In particular, particle filters suffer from potential particle impoverishment, whereas lack of environmental features can cause scan matching methods to collapse. In the current paper a multi-threaded combination of Rao-Blackwellized particle filters with a scan matching algorithm (CRSM SLAM) aiming to overcome those defects, whilst exploiting each method's advantages is presented. CRSM is employed in feature-rich environments while concurrently reducing the particle filter dispersion, whilst the particle filter allows the maintenance of the correct hypothesis in environments with scarcity of information. Finally, a method to reduce the number of particle filter resamplings, employing topological information is proposed.