Simultaneous Localization And Mapping For An AUV Using Forward-looking Sonar

In order to achieve capability of autonomous navigation for an AUV in an unknown environment,AUV should be equipped independent visual system and the positioning system. By visual system,AUV could get around environmental information and guide its autonomous navigation. Visual system rely mainly on the "Sound Vision",it is different from the traditional sonar system,Visual system not only have the ability to obtain acoustic image and information,but also have the ability of information processing,feature extraction and classification. Simultaneous Localization and Mapping is the key point of robots autonomous navigation, the study of the sound vision and SLAM is of great theoretical significance and the value of application.In the unknown complex undersea environment,the majority of sensors which are used in the air are unable to use in the undersea environment,such as optical and wireless,which attenuate too soon in the water. In the circumstances of lacking external navigation support,AUV can now only rely on the self contained inertial sensors and sonar. Forward looking sonar and its system as the main sensory (sensing equipment),charged with target location,identification and imaging tasks. It can provide the distance and angle data of the obstacles and distinguish the outline and location of target in the two dimensional space (XY plane).The advantage of using forward looking sonar is that the sonar can be equipped in the front end of the AUV,sonar detection provides the overlapping images frame,this will also help to achieve SLAM algorithm. Through the sonar image feature extraction,environmental features will be added to the constantly updated feature map,using SLAM algorithm implement autonomous navigation of the AUV.This paper using forward looking sonar—Super Seaking DST sonar to scan underwater environment,using the digital image processing to extract features from sonar images,and the nearest neighbor filter algorithm to associate data. The EKF SLAM is applied to estimate underwater robot pose and build environment feature maps. The simulation results show that,the active imaging sonar can be successfully applied to AUV navigation in unknown environment; In addition,the AUV trajectories generated in the cases of using lines features and points features and the error curve also be analyzed.