Adaptive Iterated Cubature Particle Filter for Mobile Robot Monte Carlo Localization*

2018 ◽  
Author(s):  
Yi Zhang ◽  
DaoFang Chen ◽  
HaiBo Lin ◽  
LiMing Zhao
Keyword(s):  
Monte Carlo ◽  
Mobile Robot ◽  
Particle Filter ◽  
Monte Carlo Localization

Double-resampling Based Monte Carlo Localization for Mobile Robot

2010 ◽  
Vol 36 (9) ◽  
pp. 1279-1286 ◽  
Author(s):  
Tian-Cheng LI ◽  
Shu-Dong SUN
Keyword(s):  
Monte Carlo ◽  
Mobile Robot ◽  
Monte Carlo Localization

Conjugate Unscented Particle Filter Based Monte Carlo Localization for Mobile Robots

2014 ◽  
Vol 556-562 ◽  
pp. 2266-2269
Author(s):  
Jiang Xue Fei ◽  
Song Yu
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Dead Reckoning ◽  
Transition Density ◽  
Robot Localization ◽  
Proposal Distribution ◽  
Unscented Transform ◽  
Unscented Particle Filter ◽  
Mobile Robot Localization ◽  
Monte Carlo Localization

For mobile robot localization in known environment, the 5th-order Conjugate Unscented Particle Filter Monte Carlo Localization (CUPF-MCL) algorithm is proposed. CUPF-MCL combines the 5th-order Conjugate Unscented Transform (5th CUT) with Kalman Filter to generate more accuracy particle filter proposal distribution, calculating the transition density up to the 5th-order nonlinearity. In simulation, the performance of CUPF-MCL is compared with that of dead reckoning, PF-MCL, EPF-MCL and UPF-MCL. Results show that CUPF-MCL improves the accuracy of localization.

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