The Research Of Indoor Robot Pose Based On Multi-sensor Information Fusion

With the development of computer science and sensor technology, the robotics increasingly tends toward intelligent and autonomous. In order to make the robot move in the environment autonomously and do the task, the robot must be capable of calculating its pose. Pose calculation is a very important researching in the autonomous mobile robotics. This dissertation is focused on the multi-sensor fusion based pose calculation problem for indoor autonomous mobile robot. Combining theory with practice, carry out the research of Micro inertial attitude and RFID Location. The main contents and contributions of this dissertation include the following aspects:In view of the existing multi-sensor fusion method’s low precision and computational complexity, based on micro inertial AHRS’high-precision sensors gyroscopes, accelerometers and magnetometers combination, proposed an improved Kalman filter fusion attitude method. The gyroscope data as forecast data to estimate the Kalman process covariance Q. Accelerometer and magnetometer data as observed data, Combines gyro error estimate of the measurement noise covariance R. through Kalman filter to complete the multi-sensor information fusion. Experimental results show that the proposed method is efficient in feature extraction, which can improve the accuracy and robustness of robot pose calculation.In order to improve the precision and reduce the sample impoverishment problem of autonomous localization based on Particle Filter. Present a novel approach to self-localization with passive RFID fingerprints using vector space similarity measures and weighted k-nearest neighbors (WKNN). Introduction of particle filtering’s positioning algorithm, analysis of effects of different similarity measure method on positioning precision, proposed a new similarity measure algorithm Hsim, establish the observation model. Finally, using resampling method accurately estimate the real robot pose. Experimental results show that the method effectively improves the precision and efficiency of robot pose calculation.The topic realizes the robot localization and attitude determination function in indoor environment, provides decision-making information for autonomous mobile robot. Improve the intelligent degree of dangerous environment of special robot.