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Conjugate Unscented Particle Filter Based Monte Carlo Localization for Mobile Robots

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.