Research On Key Technologies Of Simultaneous Localization And Mapping For Mobile Robot

Simultaneous Localization and Mapping (SLAM) is that a mobile robot makes selflocalization and incremental mapping in the known or completely unknown environment bymeasuring on-line and position estimating using the sensors installed on the robot. The navigationmethod, which needs no prior map, is very important for robot’s long-time autonomous operationin unmanned site.This paper comes from National Natural Science Foundation Project-“Research on the keytechnologies of swarm cooperation topography survey for micro autonomous underwater vehicle”.And the paper selected mobile robot as an studied object and finished the following keytechnology research.First, the coordinate systems for mobile robot SLAM reseach are defined. And on this basisthe models such as kinematic model, sensor observation model, environment feature model anddata association model are built. All of these have made the unified platform for SLAM keytechnologies research.Secondly, For solving the incompleteness problem of evironment feature map and theirregularity problem of natural solid landmark, a circular-class feature representation method usedto describe the position property and size property of natural solid landmark is presented.According the method, the mass center position of the solid landmark is represented by the centerposition of circular-class feature, and the property of overlook-from-space size of solid landmarkis represented by the diameter of the circular-class feature. At the meantime an environmentfeature extraction algorithm, that is Angle-Distance Cluster, is presented. The algorithm includesdata pretreatment, area segmentation and feature parameter fitting. The above algorithmfeasibility is validated by the normal data set-“Victoria Park”. An EKF-SLAM simulationalgorithm is designed and its effectiveness is validated by the simulation test of setting landmarkand robot path.Thirdly, an improved FastSLAM algorithm based on adaptive resampling is presented. In thealgorithm, the number of effective particles is calculated and the degree of particle degeneration isevaluated and judged in real time. And then the effective resampling opteration is implemented sothat the influnce of sample impoverishment caused by frequent resample is improvedeffectively.According to the similarity between particle filter and particle swarm, anotherimproved FastSLAM algorithm based on particle swarm optimization is presented. In order tomake the diversity of particle set best, the particle swarm optimization searching is led by use of the diversity heuristic factor. The feasibility and effectiveness of the algorithm are validated byself-designed simulation test.Finally, for describing SLAM data association problem clearly, the interpretation-tree modeland association matix model are discussed. And then the problem is pointed that the fixedjudgement threshold of Individual Compatibility Nearest Neighbour is not correspondent withpractice. And the fact is very easy to lead the association error. Therefore, an adaptive algorithmbased on dynamic-and-sectional threshold is presented. The effectiveness of the algorithm isvalidated by simulation test. And then the data association algorithm of Joint CompatibilityBranch and Bound (JCBB) is analyzed. And an improved JCBB algorithm, that ant swarmoptimization is applied instead of Branch and Bound searching, is presented. In this newalgorithm, the association effectiveness is judged according to the criterion-Joint MaximumLikelihood (JML). And An joint data association algorithm based on ant swarm optimization isdesigned. The new algorithm feasibility and effectiveness are validated by simulation test.