Research Of Consistency Problem Of Mobile Robot Based On EKF-SLAM Method

Robotics is a combination of advanced technologies, and also reflects a country's technological level of automation; robotics has been developed rapidly in just ten years, successfully and widely used in industrial, military, daily life and other fields. Autonomous navigation has been a hot topic in the field of mobile robots, and what provides a better autonomous mobile robot navigation technology is the simultaneous localization and mapping(SLAM), therefore the SLAM study became the new hot spot of mobile robot.Firstly, define coordinates system needed in mobile robot SLAM research, and the established mobile robot kinematic model, the sensor measurement model, environmental characteristics model and data association model based on it. And through the integrated use of these models, the general model has been built to solve the SLAM problem and a unified platform has also been built for SLAM key technologies research.Secondly,This paper investigates the convergence properties and consistency of Extended Kalman Filter(EKF) based simultaneous localization and mapping(SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a range and-bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that has been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios is clearly stated and associated theoretical proofs are provided.Final,In view of the problem that state estimation inconsistency exist in traditional EKF-SLAM(Extended Kalman Filter- Simultaneous localization and mapping, EKF- SLAM) algorithm, from the perspective of observability, a new algorithm that increasing observability constraint condition is presented,the compensation matrix U is constructed,solving constrained optimization condition,get new linear points,optimization Jacobi matrix of system, reconstruct observability matrix of system. The experiment and simulation results show that the presented algorithm is superior to the traditional EKF- SLAM algorithm in terms of state estimates accuracy and consistency of covariance. Research work and conclusions have important reference value on vehicle autonomous driving.