Research On The Simultaneous Localization And Mapping(SLAM) Problem Of Service Robot

Simultaneous Localization and Mapping(SLAM) problem in recent years has become a hot research field of service robots in recent years. Virtually, it will even deeply influence the way of human beings’ life because service robots with the technique of autonomous localization and mapping can replace humans to complete interior and exterior cleaning, field or subsea operations, disaster rescue, outer space exploration and many other tasks. In a brief generalization of the classification of service robots, research status at domestic and overseas, history of SLAM problem, the basic presentation form, two basic solutions, and so on. The problem of SLAM is divided into three sub-problems:computing complexity, environment feature representation, data association, and the theories and techniques to these problems are systematically summarized. After those questions, this thesis studies the problem of Simultaneous Localization and Mapping with service robots and the major results of the research are describe as below:1. Based on the SLAM problem in the classic solution-Extended Kalman Filter (EKF), carried out a detailed analysis and derivations, and complete simulation program in MATLAB. For the problem of EKF that unable update the the location of the landmarks which have been constructed, establish an auxiliary coefficient matrix to inherit the information between robot’s pose and position of landmarks, so as to realize the updating of map has been built.2. According to correct the posture of robot plays an importance influence on SLAM problem. Using the characteristics of EKF recursive optimal estimation and changeing the observation model, implement a methods to correct the pose of robot and the covariance matrix by make use of known landmark. Without increasing the complexity of the algorithm, it improves accuracy of positioning and mapping. Compare with the EKF algorithm, the improved algorithm’s evaluated error is smaller,positioning and mapping are more precise through the MATLAB.3. For another important solution of SLAM problem-FastSLAM algorithm, carried out a detailed mathematical derivation, and write program for FastSLAM1.0and FastSLAM2.0for simulation and comparison. The simulation results show that, FastSLAM2.0improved proposal distribution, the current observation information to join, better estimation performance in theoretical, but its derivation and practical application is more complex, in certain circumstances, their estimate performance is similar. Only under observation noise is small, FastSLAM2.0show its advantages compared to FastSLAM1.0.4. For the service robot simultaneous localization and mapping problem, made a analysis and verification of the physical experiments. Write the control program of car by Arduino1.0.5, and realizing the car’s independently moving under the experimental environment, automatic obstacle avoidance function. Write SOCKET SERVER and CLIENT program to receive and send data between PC and vehicle. For the measuring data (ultrasonic and other sensors), in MATLAB platform for data processing and some fitting, and taken the landmarks into the EKF algorithm to achieved the simultaneous localization and mapping.