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Autonomous Underwater Vehicles (AUVs) need positioning systems different than the Global Positioning System (GPS), which does not work in underwater scenarios. A possible solution to this lack of GPS signal are the Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms aim to build a map while simultaneously localize the vehicle within it. These algorithms suffer from several limitations...
In this paper we address the problem of drift-free navigation for underwater vehicles performing harbor surveillance and ship hull inspection. Maintaining accurate localization for the duration of a mission is important for a variety of tasks, such as planning the vehicle trajectory and ensuring coverage of the area to be inspected. Our approach only uses onboard sensors in a simultaneous localization...
This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The...
Different underwater vehicles have been developed in order to explore underwater regions, specially those of difficult access for humans. Autonomous Underwater Vehicles (AUVs) are equipped with on-board sensors, which provide valuable information about the vehicle state and the environment. This information is used to build an approximate map of the area and estimate the position of the vehicle within...
Visual maps of the seafloor should ideally provide the ability to measure individual features of interest in real units. Two-dimensional photomosaics cannot provide this capability without making assumptions that often fail over 3-D terrain, and are generally used for visualization, but not for measurement. Full 3-D structure can be recovered using stereo vision, structure from motion (SFM), or simultaneous...
Benthic imaging AUVs can deliver down-looking imagery with consistent altitude and illumination. These images are well suited to image matching routines that form the basis for mosaicking, vision-based Simultaneous Localisation and Mapping (SLAM) and 3D visual reconstructions. We show how these same visual constraints can be used to (1) improve the real time dead-reckoning accuracy of AUVs equipped...
Due to some communication constraints of the underwater environment, cooperative localization for AUVs is a hard task. In this paper, we present the state of the art in this area, analyse the existing solutions to related problems and propose 2 algorithms, based on the EKF, to solve the localization problem with a group of Autonomous Underwater Vehicles equipped with low cost navigation systems in...
This paper describes an approach to achieving high resolution, repeated benthic surveying using an Autonomous Underwater Vehicle (AUV). A stereo based Simultaneous Localisation and Mapping (SLAM) technique is used to estimate the trajectory of the vehicle during multiple overlapping grid based surveys. The vehicle begins each dive on the surface and uses GPS to navigate to a designated start location...
In this paper we propose an approach to SLAM suitable for bathymetric mapping by an autonomous underwater vehicle (AUV). AUVs typically do not have access to GPS while underway and the survey areas of interest are unlikely to contain features that can easily be identified and tracked using bathymetric sonar. We demonstrate how the uncertainty in the vehicle state can be modeled using a particle filter...
The paper describes a localization system for autonomous underwater vehicles (AUV). It uses a DVL (Doppler velocity log) sensor and AHRS (attitude and heading reference system) sensor to measure AUV's depth, attitude and velocities relative to the bottom. A mechanically scanning imaging sonar (MSIS) is employed to obtain acoustic images of objects in underwater environment. In order to estimate optimally...
Navigation and localization with high precision has been one of most critical issues for the safety and effective completion of missions of autonomous underwater vehicles. Since the underwater environment is extremely complex and the external sensors of what can be used are limited to only sonar, as well as information obtained has too much noise and interference, thus all of the intractable will...
This paper focuses on preliminary results of a system for online video mosaicing for ROVs operating in the proximity of the seabed. The proposed approach relies on a simultaneous localisation and mapping (SLAM) system already tested in typical operating conditions with the Romeo ROV. In particular, previous experimental activity demonstrated the possibility of defining suitable unambiguous features,...
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 the sea trial experiments conducted to validate the physical implementation of online EKF-SLAM onboard the Meredith Autonomous Underwater Vehicle (AUV) in shallow coastal waters. This paper provides details of the hardware and software implementations of the EKF-SLAM algorithms running real-time on the AUV to perform feature-based navigation. The computation resources onboard...
We propose a method to improve the geo-referenced accuracy and self-consistency of bathymetric maps generated by autonomous underwater vehicles (AUVs), where the navigation solution is prone to drift when GPS or other methods of absolute positioning are unavailable. This is accomplished using a non-feature based approach to simultaneous localization and mapping (SLAM) that utilizes a 2D grid structure...
The simultaneous localization and mapping (SLAM) algorithm could make up for the disadvantages of underwater navigation methods based on priori map, hence makes underwater vehicles to be truly autonomous. A modified data association method is proposed to lighten the systemic computational burden and improve the data association process. The method makes use of a two-step filtration to solve the ambiguities...
This paper provides a general overview of the autonomous underwater vehicle (AUV) research projects being pursued within the Perceptual Robotics Laboratory (PeRL) at the University of Michigan. Founded in 2007, PeRL's research thrust is centered around improving AUV autonomy via algorithmic advancements in sensor-driven perceptual feedback for environmentally-based real-time mapping, navigation, and...
This paper proposes a navigation scheme for an AUV to perform large-area imaging of seafloors, especially those of vent fields. The method is based on SLAM (simultaneous localization and mapping) using a profiling sonar and passive acoustic landmarks. Since real-time positioning accuracy of the method is high enough for rough photomosaicing, this method enables an efficient survey with a small overlap...
This paper describes the autonomous underwater vehicle (AUV) Sirius and presents its participation in a scientific expedition to survey drowned reefs along the shelf edge of the Great Barrier Reef (GBR) in Queensland, Australia. The primary function of the AUV was to provide geo-referenced, high-resolution optical imagery to facilitate validation of seabed habitat characterisation based on acoustic...
This paper reports the modifications involved in preparing two commercial Ocean-Server AUV systems for simultaneous localization and mapping (SLAM) research at the University of Michigan (UMich). The UMich Perceptual Robotics Laboratory (PeRL) upgraded the vehicles with additional navigation and perceptual sensors including 12-bit stereo down-looking Prosilica cameras, a Teledyne 600 kHz RDI Explorer...
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