Research Of Simultaneous Localization And Mapping For Autonomous Underwater Vehicle

Autonomous underwater vehicle (AUV) is the current research focus, which represents the future development direction of underwater vehicle technology. However, navigation is still the one of the major technical challenges of AUV, in which localization is the fundamental problem, which refers to a process of estimating robot's pose by detecting the internal state of robot and perceiving the external surrounding environment through carried sensors.Localization and mapping is integrated and interrelated in unknown environment, which is simultaneous localization and mapping (SLAM). SLAM can be described as: an autonomous mobile robot leaves from an unknown location in an unknown environment, relies on sensors to build a global environment map gradually, and then calculates its location by this map.This article first introduces the theory basis of SLAM algorithm, which is EKF, then add other sensors update to only sonar update SLAM, namely SLAM with multi-sensor update, illustrates its flow and certifies it can improve accuracy of localization and mapping for the robot from theory. Next, experimental preparation is introduced, which includes sensors carried in AUV platform and features extraction for map construction in SLAM. Extracting reliable and accurate environmental features is prerequisite for the precision of SLAM algorithm, while features representing is based on environment where the robot navigates. But the original features of direct extraction from environment are generally too dense to affect the efficiency and accuracy of SLAM algorithm, and then denoising and sparseness of features are adapted. Noise involves noise of sonar itself and environmental background noise, and sparseness aims in redundant information of each beam and adjacent beams of sonar launches.Finally, validity of SLAM with multi-sensor update is verified by lake and sea experiments. Lake experiment result proves that accuracy of localization and mapping of this algorithm is superior to SLAM only with sonar update, and longer sea trial experiment further verifies validity of SLAM algorithm. In addition, it also shows the accuracy of localization and mapping meets the requirements of autonomous navigation of robot.