Research And Application Of SLAM Algorithm Based On Multi Sensor Fusion

With the development of sensor technology and artificial intelligence technology,intelligent mobile Robots gradually replace humans for repeated work and dangerous work.In many research fields of robots,the problem of Simultaneous Localization And Mapping(SLAM)is the most important part of mobile robot's complete autonomous work.The SLAM problem can be described as: In an unknown environment,the mobile robot determines the environmental map and its position in the map by the sensors it carries.However,the single sensor has a large error.This thesis implements SLAM algorithm based on multi-sensor fusion.The binocular camera can recover the depth information of the spatial landmarks through stereo matching.Compared with other sensors such as laser,the binocular camera has better robustness and richer information.The inertial measurement unit(Inertial Measurement Unit,IMU)has a higher accuracy in a shorter time.By combining the advantages of IMU and binocular camera,an improved FastSLAM algorithm for fusion of IMU and binocular cameras is proposed in this thesis.Firstly,This thesis studies the principle of binocular stereovision.This thesis compares the commonly used feature algorithms of stereo matching and finally the ORB(Oriented FAST and Rotated Brief)algorithm is chosen as the feature point extraction algorithm.This thesis improves the extraction of ORB algorithm and reduces false matching.This thesis improves the extraction of ORB algorithm and reduces false matching.Before the feature points are acquired,the image is preprocessed by the histogram equalization method with limited contrast,which reduces the influence of noise.This paper uses the Hamming distance setting threshold method,Random Sample Consensus(RANSAC)and matching pair distance ratio and angle ratio mis-matching screening algorithm to eliminate false matching,which obviously reduces the number of mis-match.Secondly,the motion process model and the observation model of the SLAM algorithm are analyzed.The FastSLAM algorithm based on particle swarm optimization may make the sampling particles lose diversity and make the particle swarm to the local optimal solution.So this thesis makes improvements based on existing particle swarm FastSLAM algorithm.By introducing immune algorithm to increase mutation and multiplication of particles and increase particle diversity,the problem of particle degradation during Fast SLAM iteration and particle exhaustion caused by multiple resampling is improved.Finally,based on the improved FastSLAM optimization algorithm,the SLAM system with binocular camera and IMU is realized in this thesis.And this system was developed under the Robot Operating System(ROS).This system was tested in a realworld scenario and the effectiveness of the improved FastSLAM algorithm was verified.