Distributed map fusion method based on adaptive Kalman filtering and average tracking

Abstract

The invention provides a distributed map fusion method based on adaptive Kalman filtering and average tracking, and relates to the technical field of control and information. The method comprises thefollowing steps: firstly, constructing a network topological structure diagram of the multi-agent system, and determining an adjacent matrix; respectively establishing a state equation and an observation equation of each intelligent agent according to a motion sensor and a measurement sensor used by the intelligent agent; designing an adaptive Kalman filtering algorithm according to the state equation and the observation equation of each intelligent agent, obtaining local map information of each intelligent agent, converting the local map information into a form of an information vector and aninformation matrix, and designing and realizing a distributed map fusion method by utilizing an average tracking algorithm; and setting initial value information and road sign point information of aplurality of agents and statistical magnitude initial values of process noise and observation noise, operating a distributed map fusion algorithm, and continuously correcting the distributed map fusion algorithm according to an operation result until the algorithm converges, thereby finally obtaining a global map of an agent detection area.

Description

technical field [0001] The invention relates to the field of control and information technology, in particular to a distributed map fusion method based on adaptive Kalman filtering and average tracking. Background technique [0002] Since the late 1990s, the problem of simultaneous localization and mapping (SLAM) for a single agent has been the focus of attention. Simultaneous positioning and mapping (SLAM) refers to the fact that without the assistance of non-autonomous navigation methods, such as GPS, Beidou and other positioning systems, the agent relies on sensors for its own position estimation and sensors for obtaining environmental information to realize its own positioning. Simultaneously the process of mapping the detection area. When a single agent performs the SLAM process, if the noise statistics in the system model are unknown, then its accurate system model is unknown; at the same time, as time goes by, errors will accumulate in environmental sensors, resultin...

AI Technical Summary

Problems solved by technology

Moreover, since local maps are often represented by state vectors and covariance matrices, it is difficult to solve the cross-covariance between multiple agents in this representation

Images