Abstract
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). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. 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. 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. 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. 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. 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. 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. 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. 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.
Highlights
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
For the purpose of avoiding obstacles, the Autonomous Underwater Vehicle (AUV) could be equipped with a forward-looking sonar (FLS) to sense the working environment at a certain distance in the direction of navigation
The augmented extended Kalman filter (EKF) (AEKF) achieves more accuracy and estimates the robot pose more robustly due to the landmark positions obtained by our improved Otsu Threshold Segmentation Method (TSM), than those detected by the maximum entropy TSM
Summary
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. While the robot moves through the underwater environment, it is exploring the region without having exact geo-referencing data, so it uses sensor measurements to perform two basic operations: one is updating concurrently its own state estimate and refining the previously observed landmark positions in the environment, the other is adding newly detected features into the state of the overall system. There exist robust methods for mapping environments that are static, structured, and of limited size These methods deal with issues related to computational complexity, data association and representation of the environment [7,8]. The section of this paper presents the related works about the state of the art of the underwater SLAM problem, and three kinds of currently used maps in mobile robot navigation systems are compared, being the landmark map the most suitable one to represent the undersea environment.
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