A Simultaneous Localization and Mapping Method under the Framework of Stochastic Finite Sets

Abstract

The invention discloses a synchronous positioning and map constructing method under a random finite set framework, and the method is applied to the field of mobile robot environment perception and composition. The method is mainly characterized in that: a random finite set theory is utilized to model an SLAM problem, both sensor observation information and an environment map are expressed in a random finite set form, and a plurality of kinds of uncertain information are completely described; after Rao-Blackwellised decomposition is carried out on SLAM total probability distribution, unscented transformation is introduced, and a proposal distribution function of particle filtering used for robot state estimation is generated by UKF, and when environment map PHD function filtering is realized by a Gaussian mixture model, the UKF is fused with the observation information to realize the updating of Gaussian members; and finally, in order to prevent the calculation amount of the algorithm to rapidly rise along with the iteration process, merging and pruning mechanisms of the Gaussian members are introduced, and the calculation amount of the algorithm is controlled in a reasonable range. According to the invention, the estimation precision and robustness of the system in a non-linear non-Gaussian problem processing process are effectively improved.

Description

Technical field [0001] The invention relates to the field of mobile robots, in particular to a method for synchronous positioning and map construction of mobile robots under a random finite set frame. Background technique [0002] A mobile robot is a comprehensive intelligent system that integrates various functions such as environment perception, data fusion, task planning, and behavior execution. It has been widely used in various fields of social production and life. The primary condition for the intelligentization of a mobile robot is to be able to move autonomously, and the achievement of this condition requires the robot to obtain a map description of the external environment it is in and its own location information in the environment, that is, composition and positioning. In the early research, the two were considered to be completely independent topics, and there have been some solutions for autonomous localization of robots in a known environment and map construction of...

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Problems solved by technology

[0007] The purpose of the present invention is to make a solution to the problem that the SLAM method under the existing stochastic finite set model cannot handle the non-linear PHD function when using PHD filtering for map estimation and the generation of the proposed distribution function when using particle filtering for robot pose estimation. Corresponding improvement, thereby improving the estimation accuracy and reliability of the algorithm, so that the application of this type of method is expanded

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