Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

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AUV Navigation and Localization: A Review

Efficient path-planning algorithms are a crucial issue for modern autonomous underwater vehicles. Classical path-planning algorithms in artificial intelligence are not designed to deal with wide continuous environments prone to currents. We present a novel Fast Marching (FM)-based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a 2-D continuous path from a discrete representation of the environment. Second, we take underwater currents into account thanks to an anisotropic extension of the original FM algorithm. Third, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic media. Finally, a multiresolution method is introduced to speed up the overall path-planning process

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Path Planning for Autonomous Underwater Vehicles

A survey of underwater vehicle navigation: Recent advances and new challenges

Application of robotics in offshore oil and gas industry- A review Part II

Image pattern classification for the identification of disease causing agents in plants

This paper describes a new framework for segmentation of sonar images, tracking of underwater objects and motion estimation. This framework is applied to the design of an obstacle avoidance and path planning system for underwater vehicles based on a multi-beam forward looking sonar sensor. The real-time data flow (acoustic images) at the input of the system is first segmented and relevant features are extracted. We also take advantage of the real-time data stream to track the obstacles in following frames to obtain their dynamic characteristics. This allows us to optimize the preprocessing phases in segmenting only the relevant part of the images. Once the static (size and shape) as well as dynamic characteristics (velocity, acceleration,...) of the obstacles have been computed, we create a representation of the vehicle's workspace based on these features. This representation uses constructive solid geometry (CSG) to create a convex set of obstacles defining the workspace. The tracking takes also into account obstacles which are no longer in the field of view of the sonar in the path planning phase. A well-proven nonlinear search (sequential quadratic programming) is then employed, where obstacles are expressed as constraints in the search space. This approach is less affected by local minima than classical methods using potential fields. The proposed system is not only capable of obstacle avoidance but also of path planning in complex environments which include fast moving obstacles. Results obtained on real sonar data are shown and discussed. Possible applications to sonar servoing and real-time motion estimation are also discussed.

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Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar

This paper describes and evaluates a concurrent mapping and localization (CML) algorithm suitable for localizing an autonomous underwater vehicle. The proposed CML algorithm uses a sidescan sonar to sense the environment. The returns from the sonar are used to detect landmarks in the vehicle's vicinity. These landmarks are used, in conjunction with a vehicle model, by the CML algorithm to concurrently build an absolute map of the environment and to localize the vehicle in absolute coordinates. As the vehicle moves forward, the areas covered by a forward-look sonar overlap, whereas little or no overlap occurs when using sidescan sonar. It has been demonstrated that numerous reobservations by a forward-look sonar of the landmarks can be used to perform CML. Multipass missions, such as sets of parallel and regularly spaced linear tracks, allow a few reobservations of each landmark with sidescan sonar. An evaluation of the CML algorithm using sidescan sonar is made on this type of trajectory. The estimated trajectory provided by the CML algorithm shows significant jerks in the positions and heading brought about by the corrections that occur when a landmark is reobserved. Thus, this trajectory is not useful to mosaic the sea bed. This paper proposes the implementation of an optimal smoother on the CML solution. A forward stochastic map is used in conjunction with a backward Rauch-Tung-Striebel filter to provide the smoothed trajectory. This paper presents simulation and real results and shows that the smoothed CML solution helps to produce a more accurate navigation solution and a smooth navigation trajectory. This paper also shows that the qualitative value of the mosaics produced using CML is far superior to those that do not use it.

Concurrent mapping and localization using sidescan sonar

This paper describes a concurrent mapping and localisation (CML) algorithm suitable for localising an autonomous underwater vehicle (AUV). The proposed CML algorithm uses a standard off-the-shelf sonar for sensing the environment. The returns from the sonar are used to detect targets in the vehicle's vicinity. These targets are used in conjunction with a vehicle model by the CML algorithm to concurrently build an absolute map of the environment and localise the vehicle in absolute coordinates. In order for the algorithm to work, the stored targets must be associated to the sonar returns at each iteration. Given the nature of sonar data, false returns complicate this process. The choice of targets and a suitable data association strategy is, therefore, vital. The chosen targets consist of returns of a significant strength. The segmentation detects these targets and calculates (a) the relative position of their center of mass with respect to the vehicle, (b) the targets' surface size, and (c) the targets' first invariant moment. This information is used by the system to perform the data association. We have chosen to adapt the well known multiple hypothesis tracking filter (MHTF) to the CML structure. This is a measurement oriented approach that finds the probability that an established target gave rise to a certain return. The paper presents results with real sonar data.

Feature extraction and data association for AUV concurrent mapping and localisation

Considers two well-known pattern recognition techniques using texture analysis. The first is the co-occurrence matrix method which relies on statistics and the second is the Kohonen map which comes from the artificial neural networks domain. Both methods are used as feature extraction methods. The extracted feature vectors are fed to a second Kohonen map used as classifier. The authors report briefly some results of their experimental assessment of the merit of each technique when applied to the problem of classifying the seabed from sequences of real images.

Texture analysis for seabed classification: co-occurrence matrices vs. self-organizing maps

This paper deals with automatic target tracking in video and sonar subsea sequences, an essential capability for automating tasks currently performed by remotely operated vehicles under pilot control. We describe two trackers, one for video sequences, the other for sector scan sonar sequences. No assumptions are made about the images, scene, or motion observed. To illustrate applications, we report results of our systems for 3-D structure reconstruction and panoramic mosaic building from video sequences and describe in some detail our path planning and obstacle avoidance system using sonar sequences.

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I. Tena Ruiz

Papers

Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar

Concurrent mapping and localization using sidescan sonar

Feature extraction and data association for AUV concurrent mapping and localisation

Texture analysis for seabed classification: co-occurrence matrices vs. self-organizing maps

Feature Tracking in Video and Sonar Subsea Sequences with Applications