![]() ![]() Underwater SLAM is a tough variable dimension state estimation problem in which the size of the state space is increased or decreased when features are added or removed from the map. The SLAM algorithm jointly estimates the geometric landmark locations and the vehicle pose with the help of a stochastic framework. The second one is an augmented EKF (AEKF)-based underwater landmark SLAM, which employs the landmarks detected by the improved Otsu TSM. This paper comes up with two proposals and their combination: the first one is an improved Otsu Threshold Segmentation Method (TSM), which provides a real time and accurate segmentation solution for underwater feature detection and has been proved to be faster than other TSMs. Additionally, the linearization errors caused by the standard extended Kalman filter (EKF) can also severely degrade the performance of the localization estimates. The use of the conventional Kalman filter (KF) method is not adequate for estimating underwater landmark positions, as it suffers from the assumption of Gaussian noise statistics, which often lead to failures when these assumptions do not hold. An accurate map is a fundamental and mandatory requirement for a robot to work autonomously. Robotic mapping is the process of generating a spatial representation of a given environment from a series of sensor measurements observed by the robot while travelling through that environment. In recent years, underwater vehicles are increasingly being used in complex environments like seas, harbors or dams, and underwater robotic mapping has received considerable attention from the research community. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. ![]() During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. ![]() As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. ![]() The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). ![]()
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