A fastslam method based on improved particle proposal distribution and adaptive particle resampling

Abstract

The invention discloses a FastSLAM method based on particle proposal distribution improvement and adaptive particle resampling. The method comprises the steps that 1, a square root transformation unscented Kalman filter is utilized to estimate optimal particle proposal distribution, and the pose state of a robot is sampled; 2, a square root volume Kalman filter is utilized to update feature map information corresponding to each particle; 3, an adaptive particle resampling method based on relative entropy is utilized to determine the quantity of particles needed at the current moment; 4, the pose stat of the robot and guidepost feature map information are determined according to a particle set obtained after resampling. A traditional FastSLAM algorithm is improved from the two aspects of quality and quantity of the sampling particles at the same time, thus, the numerical stability and accuracy of the algorithm in the estimation process are enhanced, and the quality of the sampling particles is improved; in the particle resampling process, the least quantity of the needed particles is dynamically determined according to estimation uncertainty, and therefore the calculation efficiency of the algorithm is improved.

Description

technical field [0001] The invention belongs to the field of autonomous navigation of mobile robots, in particular to a FastSLAM method based on improved particle proposal distribution and adaptive particle resampling. Background technique [0002] As an important branch of intelligent robots, mobile robots have been widely used in fields such as manufacturing industry, national defense and military, aerospace, health care, and home services. In order to realize that mobile robots can successfully complete specific tasks without manual intervention in complex and unknown environments, mobile robots must have the ability to navigate autonomously. As a key prerequisite for autonomous navigation, a mobile robot needs to estimate its own attitude through the internal and external sensors it carries, and at the same time describe the unknown environment with a map. The Simultaneous Localization and Mapping (SLAM) method of mobile robots can make full use of the correlation betwe...

AI Technical Summary

Problems solved by technology

However, when the dimension of the state vector is high, the weight of the central Sigma point of the tasteless Kalman filter may be negative, so the positive definiteness of the posterior covariance matrix cannot be guaranteed; the volume Kalman filter is used to limit the sphere radius of the volume point is proportional to the dimension of the state vector, so the so-called non-local sampling problem arises

Images