Abstract
Localization is the primary problem of mobile robot navigation. Monte Carlo localization based on particle filter has better accuracy and is easier to implement, but there is also the problem of particle degradation. In this paper, the iterative extended Kalman filter is optimized by the Levenberg‐Marquardt optimization method. An improved particle filter algorithm based on the upon optimized iterative Kalman filter is proposed, and the importance probability density function of the particle filter is generated by the maximum posterior probability estimation of the improved iterative Kalman filter. Simulation results of the improved particle filter algorithm show that the algorithm can approximate the state posterior probability distribution more closely with fewer sampled particles under the premise of ensuring sufficient state estimation accuracy. Meanwhile, the computation is reduced and the real‐time performance is enhanced. Finally, the algorithm is validated on the indoor mobile service robot. The experimental results show that the localization algorithm’s accuracy meets requirement for real‐time localizing of the restaurant service robot.
Highlights
Localization is the primary problem of mobile robot navigation
According to the global localization requirement of indoor restaurant service robot which is driven by two differential wheels, the kinematics model of the service robot and the localization model through the global ceiling camera are established in this paper firstly
A particle filter algorithm based on an improved iterative Kalman filter is proposed, and the importance probability density function of the particle filter is generated by the maximum posterior probability estimation of the improved iterative Kalman filter
Summary
Localization is the primary problem of mobile robot navigation. Accurate, fast. and stable localization is the premise for mobile robot to perform navigation tasks correctly [1]. When the environmental observation noise of mobile robots is low (i.e., the environmental observation sensor is too accurate, such as the laser sensor), particle set degradation is easy to occur, which makes the SLAM algorithm diverge [15] It is a simple and widely used method of selecting the state transfer probability density function with prior property as the importance probability density function to solve the phenomenon of particle degradation [15]. Ullah et al designs importance density function by using unscented Kalman filter and proposes an unscented particle Filter (PFUKF) algorithm It works well at the cost of large computation burden and when the state estimation error covariance is close to the process noise covariance [17]. Inspired by the Gauss-Newton optimization algorithm, this paper improves iterative extended Kalman filtering using the Levenberg-Marquardt optimization method to improve the convergence and calculation accuracy of IEKF iterative sequence. It is applied to the global visual localization on the restaurant service robot and verified by experiment
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