EKF-SLAM algorithm of self-adaptive dynamic observation domain

Abstract

The invention provides an EKF-SLAM algorithm of a self-adaptive dynamic observation domain, and aims to solve the problem of low calculation efficiency of a standard EKF-SLAM algorithm in a large andcomplex environment due to excessive state vector and error covariance dimensions. By adjusting the observation range in real time, it is ensured that the number of feature points observed at any moment is within a certain set interval, and redundant feature points exceeding the constraint range are removed from the state vector and the error covariance. Based on the basis, the invention further includes estimating the pose of the vehicle and the position of the feature point. Compared with a standard EKF-SLAM algorithm, the algorithm provided by the invention has the advantages that the calculation time is saved by more than 60%, and the calculation efficiency is greatly improved while relatively high estimation precision is ensured. It can be speculated that when the number and density of the feature points in the environment are further increased, the advantages of the algorithm are more obvious.

Description

technical field [0001] The invention belongs to the technical field of navigation, and in particular relates to an EKF-SLAM algorithm of an adaptive dynamic observation domain. Background technique [0002] In recent years, the concept of simultaneous localization and mapping (SLAM) has developed sufficiently mature. At present, the SLAM problem can be described as the vehicle starts to move from an initial position in a completely unknown environment. During the movement, the vehicle-mounted sensor is used to observe the feature points in the environment and establish an environment map, while continuously updating the vehicle itself in the environment. position and posture. Among many SLAM algorithms, the SLAM algorithm based on extended Kalman filter (EKF-SLAM) is widely used. However, there is a defect in the standard EKF-SLAM algorithm: in the case of a large environment and a high density of feature points, as the observed feature points expand, the dimensionality of...

AI Technical Summary

Problems solved by technology

However, there is a defect in the standard EKF-SLAM algorithm: in the case of a large environment and a high density of feature points, as the observed feature points expand, the dimensionality of the state vector and error covariance in the algorithm will become very large , which in turn leads to a reduction in computational efficiency and affects the real-time computational performance of the algorithm

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