Research On Simultaneous Localization And Mapping For Autonomous Underwater Vehicle

Navigation is the premise and basement of performing tasks for autonomous underwatervehicle (AUV). Error of inertial navigation and dead reckoning accumulates over time, soAUV must float to the suface of water periodically to correct the position by GPS which isnot fit for hidden mission. This paper concentrates on navigation of AUV with partial or nonepriori information in structured environment. AUV could achieve autonomous navigation andbuilds the environment map by environmental perception sensor, position and attitude sensors,which has a great theory significance and practice value to longtime and safe work of AUV.Firstly, the basic framework of SLAM was designed. Environment map model, featuremodel, coordinate system, kinematic model of AUV and measurement model of sensors wereestablished. The work above is the basis of the research for the following SLAM.Secondly, feature extraction of SLAM was reasearched deeply. Large memory capacityand low efficiency exist in traditional Hough transform. To solve the problem, featureextraction of marine environment was proposed based on fuzzy adaptive Hough transform.Sonar data was processed fuzzily with the information from gradient direction. Minimaxfuzzy reasoning was used to judge the probability that one data belongs to one line. Data thatparticipat in voting were selected adaptively. The line features of the port were extracted.Compared with traditional Hough transform, the method proposed has the advantage of smallmemory capacity and high efficiency and strong practicality.Thirdly, data association of SLAM was studied. To solve the contradiction betweenaccuracy and computational efficiency, ICNN-JCBB rapid swich data associatin based ongray prediction was designed. Gray theory was used to predict the feature density ofenvironment. A threshold was set to switch association method quickly. Simulations showthat the method proposed can improve data association efficiency with high accuracy.Fourthly, AUV position estimation method for SLAM was researched. In EKF SLAM,kinematic model of AUV can not match the actual model perfectly, and noise statisticalproperties are not accurate which makes the navigation accuracy of EKF-SLAM low.Sage-Husa adaptive EKF-SLAM was proposed to solve the problem above. The uncertaintyof model and statistics of noise were considered as process noise of system. Recursivefiltering was carried on based on observation data. With the estimatior of time-varying noise,the noise statistical properties were estimated and revised, which ruduces the impact of modelerror, and accuray of filter is improved. Experiment with trial data shows that different initial values had big influence on Sage-Husa adaptive EKF-SLAM. To sovle the prolem of initialnoise value, combined adaptive EKF-SLAM was designed based on Sage-Husa adaptiveEKF-SLAM and strong tracking EKF-SLAM. The convergence criterion of residual was usedto judge the estimation divergence. Simulation result with trial data shows that combinedadaptive EKF-SLAM isn’t affected by the initial noise value which can ensure the estimationaccuracy of the position of AUV and features to some extent.Finally, AUV position estimation method based on FastSLAM was researched to solvethe influence of nonlinear of model and non-Gaussian noise in EKF-SLAM. Particledegeneracy and impoverishment exist in FastSLAM. Linear optimization resamplingFastSLAM was designed. In the process of resamplng, the combinations of coied and lostparticles were done. Particles with bigger weight were selected from particles produced whichcan reduce the pressure of simple copy. The information carried by particles with smallweight can be reserved. Simulations with trial data show that linear optimization resamplingFastSLAM can reduce the particle impoverishment. Compared with standard FastSLAM, theposition estimated accuracy of AUV and features are enhanced. But the accuracy is stillinfluenced by the losing of little particles. Variance reduction of particle weights FastSLAMwas designed to avoid losing particle. An adaptive exponential fading factor was produced bycooling function of simulated annealing. With the weight rising of small weight particle andreducing of big weight particle, the variance of particles is reduced, and the effective particlenumber is improved. Simulation based on trial data shows that the method proposed can avoidparticle degeneracy, the accuracy of AUV navigation and map building were improved.