Research On Indoor Environment Feature Observation And Data Association Method For Mobile Robot

In the aspect of intelligent mobile robots,realizing the autonomy of mobile robots is the direction that scholars all over the world have been working hard.In an unknown environment,if a mobile robot wants to perform a certain task independently,it must know the surrounding environment and its position in the environment.This requires the sensor itself to sense the surrounding environment and determine the position.Under this premise,some scholars have proposed the problem of simultaneous positioning and map construction(SLAM),that is,in an unknown environment,the robot perceives the surrounding environment through the sensors carried by itself,continuously updates the construction environment map and constantly updates its position,which has always been the field of mobile robots.The paper studies the key technical feature observations and data associations in the SLAM problem,and finally uses the mobile robot to verify the algorithm in the real environment.Firstly,the coordinate system of the problem is established,then build the SLAM system model,including the motion model of the mobile robot,the laser sensor measurement model,the environmental map representation model,and the environmental feature model.Secondly,the extraction method of environmental line features is designed,and the characterization method of environmental features is studied.A new line feature representtation method is proposed.By setting the dynamic threshold to preliminarily divide the characteristics of the environment points,multiple point feature regions are obtained.Then,the traditional segmentation-aggregation algorithm is improved to further segment the feature regions,and the dynamic threshold segmentation strategy is used to obtain the final point set region.Finally,The least square method is used to fit the straight line,and the method of determining the end point of the line segment is proposed to determine the position of the line segment on the straight line,and the verification is based on the real environment data.Thirdly,the data association method of line-oriented features is deeply studied.The basic concepts and mathematical descriptions of data association are introduced,and the mathematical model is given.Then,in the indoor environment,the correlation accuracy rate is low for the independent compatible nearest neighbor data association method.An easy-to-mismatch problem has been proposed to improve the method.Firstly,the principle of the standard ICNN data association algorithm is introduced.The limitations of the algorithm are analyzed through experiments.The calculation method of the necessary knowledge of Mahalanobis distance is given.Then,the limitations of the standard ICNN are analyzed.Based on the positional relationship between the line segments,the mismatch matching rule is proposed.Then the associated feature retention principle and the associated correct rate judgment model are designed and verified.Finally,a data association method based on ant colony algorithm is proposed,which is used for the search matching problem when the number of features is increasing.It pays attention to the correct rate and real-time of data association.Finally,an extended Kalman filter SLAM method based on line features is designed,which includes the basic principle of EKF filtering and the EKF-SLAM method steps based on line features.Finally,based on the real indoor environment,the mobile robots equipped with laser range finder and odometer are used to collect environmental data.The environmental map obtained by the algorithm can further prove the effectiveness of the algorithm.