RBPF-gmapping

Summary

RBPF divides the slam problem into two parts: localization and mapping

drawbacks: the number of particles  and the particle-depletion problem

challenges: reduces the quantity of particles

contributions in paper [ Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters---Giorgio Grisetti, Cyrill Stachniss, and Wolfram Burgard ]

1. the improved proposal distribution considering the movement of robot and the recent observation that gives accurate manner

2. adaptive resampling technique that reducing the risk of depletion  

Algorithm

1.mapping with known poses P(m| x, z)

2.estimate the trajectories P(x| z, u) using PF

 PF———E.X. SIR

procedure：1) sampling from proposal x(i) [how to compute proposal]

                    2) importance weighting w(i) —— give a recursive formulation

                    3) resampling  [when the resampling step should be carried out]

                     4) map estimation P(m(i)| x(i), z)

Part I : improved proposal distribution

used: odometry motion model——easy to compute but suboptimal when the sensor more precise than odometry

improved: integrating the most recent sensor observation z into proposal——optimal respect to weights——gaussian approximation for each particle

how to compute the gaussian approximation

1.scan matcher to determine the meaningful area L of likelihood function

where scan matcher can use lots of algorithms, and gradient-descent search

2.sampling in the L and evaluate the points

Part II : adaptive resampling

resampling can decrease the computation but maybe remove the good particles

give a N(eff) which represent the as threshold N/2