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This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and...
This paper describes a an SLAM algorithm for the navigation for an indoor autonomous mobile robot. The main emphasis of this paper is on the ability of line extraction. A recognition method based on straight line extraction is proposed for extracting the key features on the office ceiling, in an effort to estimate the pose of mobile robot. Random sample consensus (RANSAC) paradigm is used to group...
This paper deals with monocular SLAM of a mobile robot in an indoor environment where visually similar features (so-called indistinguishable features) exist on the ceiling. When these indistinguishable features exist near a feature, data association suffers from false matches which lead to localization failure. To reliably estimate the robot pose in various environments, the proposed scheme uses the...
A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association...
One of the main contributions of this article is related to the multirate asynchronous filtering approach for the SLAM problem based on PFs. Previous multirate filter contributions are mainly for linear systems. A Kalman filter is applied for linear quadratic regulator (LQG) control, while in a Kalman filter is developed using lifting techniques. In this article, significant improvements for robot...
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