Monte-Carlo location algorithm based on self-adaptation iteration volume particle filter

Abstract

The invention provides a Monte-Carlo location algorithm based on a self-adaptation iteration volume particle filter, and belongs to the technical field of robot location methods. According to the Monte-Carlo location algorithm based on the self-adaptation iteration volume particle filter provided by the invention, the improvement is carried out on the basis of a CMCL (Monte-Carlo location of volume particle filter) algorithm, and the new location algorithm is provided aiming at the problems of high computing capacity and poorer real-time processing capacity existed in a current CMCL algorithm.According to the Monte-Carlo location algorithm based on the self-adaptation iteration volume particle filter provided by the invention, a Gauss-Newton iteration volume Kalman filter is used for generating the volume particle filter with significant proposal distribution; a Kullback-Leibler distance criterion is utilized for enhancing CPF (Continuation Power Flow), so that the particle quantity is self-adaptively selected. According to the Monte-Carlo location algorithm based on the self-adaptation iteration volume particle filter provided by the invention, the limitation of high-order truncation errors of the standard volume particle filter can be effectively avoided, the location error and the algorithm calculation quantity are reduced, and the processing capacity is improved.

Description

technical field [0001] The invention relates to the technical field of robot positioning methods, in particular to a Monte Carlo positioning algorithm based on adaptive iterative volumetric particle filtering. Background technique [0002] Mobile robot positioning utilizes the prior environment map information, the pose estimation at the previous moment, and the observation information of the sensor. After a series of processing and transformation, the estimation of the current pose is generated, so as to determine its position in the working environment. The sensors are able to measure the distance between the robot and the nearest obstacle in all directions. [0003] The Monte Carlo Localization (MCL) algorithm based on particle filter uses the prior distribution instead of the posterior distribution for sampling, and combines the observation likelihood function to evaluate the importance weight of each particle, ignoring the current mobile robot environment The correctio...

AI Technical Summary

Problems solved by technology

However, the linearization process of the nonlinear system in the volumetric Kalman filter leads to higher order truncation errors

For each iteration in the MCL algorithm, the particle set must be obtained through sequential importance sampling, and the corresponding importance weights must be calculated, resulting in a large amount of calculation and weak real-time processing capabilities.

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