Robot autonomous navigation method based on heading-assisting distributed type SLAM (Simultaneous Localization and Mapping)

Abstract

The invention discloses a robot autonomous navigation method based on heading-assisting distributed type SLAM (Simultaneous Localization and Mapping) and belongs to the field of robot autonomous navigation. According to the method disclosed by the invention, aiming at combining characteristics of a robot movement model and an observation model, factors which have relatively great influences on system performances, including calculation quantity and the like of particle filtering and expanded Kalman filtering, are compared respectively under a framework of distributed type filtering operation, and requirements on the calculation quantity in a real-time system are considered; a filter is designed by adopting a distributed type EKF algorithm, but not adopting a PF algorithm; and in order to guarantee the observability of the system at the same time, a magnetic compass is introduced to be used as an auxiliary sensor except that a traditional speedometer and a laser sensor are used as a sensor combination scheme, so that navigation information of a robot is added into the system. The design disclosed by the invention aims at the observation model of the distributed type filtering structure, and a filtering method is planned again through introducing the navigation information, so that the robot autonomous navigation aim is realized and the stability and precision of the system are improved.

Description

technical field [0001] The invention relates to robot simultaneous positioning and mapping (SLAM), which belongs to the category of robot autonomous navigation. Aiming at the problem of how to determine its own position and perceive the external environment for an autonomous mobile robot, the SLAM system aims to complete autonomous movement through the robot system model and the corresponding filtering method. Localization of the robot and mapping of the surrounding environment. Background technique [0002] SLAM (Simultaneous Localization and Mapping) is synchronous positioning and map construction. Its basic principle is: when the robot is in an unknown environment, it collects its own motion status and surrounding environment through its own sensors. No matter what filtering method is used to estimate the position, it needs to experience prediction. Compared with the update process, the design of traditional 2D-SLAM generally uses the information collected by the odometer...

AI Technical Summary

Problems solved by technology

[0005] Although the distributed structure has a relatively fixed dimension of the state matrix, it can solve the problem of computational complexity and instability, but due to the small number of landmark points in the sub-filter, the observability cannot be effectively guaranteed, that is, when there is only one observation point information When the system is running for a long time, it cannot be guaranteed that the system is observable during the long-term running process. The particle filter method has little impact on the observability, but the problem of calculation amount makes it not easy to apply to the real-time system.

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