Article

Adaptive SLAM algorithm with sampling based on state uncertainty

Inst. of Artificial Intell. & Robot., Xi'an Jiaotong Univ., Xi'an, China
Electronics Letters (impact factor: 0.96). 03/2011; DOI:10.1049/el.2010.3476 pp.284 - 286
Source: IEEE Xplore

ABSTRACT Since the uncertainty of a robot state changes over time, proposed is an adaptive simultaneous localisation and mapping (SLAM) algorithm based on the Kullback-Leibler distance (KLD) sampling and Markov chain Monte Carlo (MCMC) move step. First, it can adaptively determine the number of required particles by calculating the KLD between the posterior distribution approximated by particles and the true posterior distribution at each step. Secondly, it introduces the MCMC move step to increase the particle variety. Both simulation and experimental results demonstrate that the proposed algorithm can obtain more robust and precise results by computing the number of required particles more accurately than previous algorithms.

0 0
 · 
0 Bookmarks
 · 
15 Views
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

adaptive simultaneous localisation
 
experimental results
 
Kullback-Leibler distance
 
MCMC
 
MCMC move step
 
particle variety
 
particles
 
posterior distribution approximated
 
previous algorithms
 
proposed algorithm
 
robot state changes
 
true posterior distribution
 

J.H. Zhu