Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

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

https://repositorio.inesctec.pt/bitstream/123456789/3974/1/P-00M-QZS.pdf

Survey on advances on terrain based navigation for autonomous underwater vehicles

Purpose – The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research.Design/methodology/approach – The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms.Findings – As real‐world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and...

/pdf/towards-autonomous-localization-and-mapping-of-auvs-a-survey-4jhmahocr4.pdf

Towards autonomous localization and mapping of AUVs: a survey

A method of image-tracking by using an image capturing device The method comprises: performing an image-capture of a scene by using an image capturing device; and tracking movement of the image capturing device by analyzing a set of images by using an image processing algorithm

Image-based tracking

Navigating an autonomous underwater vehicle (AUV) is a difficult task. Dead-reckoning navigation is subject to unbounded error due to sensor inaccuracies and is inapplicable for mission durations longer than a few minutes. To limit the estimation errors a global referencing method has to be used. SLAM (Simultaneous Localization And Mapping) is such a method. It uses repeated recognition of significant features of the environment to reduce the estimation error. Devices for environment sensing that are used in most land applications like cameras, laser scanners or GNSS signals cannot be used under water: GNSS signals are attenuated very strongly in water and light propagation suffers mainly from turbid water. In more than a few hundred meters water depth there is also no sunlight. Sonic waves suffer much less from these problems and therefore sonar sensors are the prevalent sensor type used under water. A main difficulty is to extract three-dimensional information from side-scan sonar images to perform SLAM as this is an ill-posed inverse problem. An overview of existing approaches to underwater SLAM using sonar data is given in this paper. A short outlook to the system that will be used in the TIETeK project is also presented.

Deep-sea AUV navigation using side-scan sonar images and SLAM

Structure from motion refers to a technique to obtain 3D information from consecutive images taken with a moving monocular camera. In order to do this, the camera motion performed between two consecutive images needs to be known. In the work reported in this contribution, we investigated the precision of the odometry data of a commercially available passenger car. In order to identify the required precision, we developed an error model based on camera parameters and the bicycle model. We investigated two options, both being based on speed measurements. The first one uses steering angle measurements, the second one uses measurements of the yaw rate. Concluding, we found out that the specified precision of all odometry data available is sufficient to solve structure from motion. Long-term measurements empirically confirm the precision values given in the specification. This result encouraged us to actually implement a structure-from-motion approach which yields depth information as predicted from the theoretical considerations. Further work needs to be carried out in order to compensate for roll motions.

Odometry-Based Structure from Motion

Dead-reckoning navigation in the deep sea is subject to errors due to accumulation of sensor inaccuracies. As no global referencing method exists for the deep sea like, e.g., GNSS (global navigation satellite system) for land or airborne vehicles other referencing solutions need to be employed. SLAM (Simultaneous Localization And Mapping) is a technique that exploits significant environmental features to reduce the positioning error of a vehicle and to simultaneously build a map of the mission environment. It is crucial for SLAM methods to recognize places that have been visited before. In many cases this is done by extracting salient features from the environment. Obtaining those landmarks from side-scan sonar data is a challenging task as the side-scan sonar data does not consist of spatial information but rather represents an echo amplitude over time. In Coiras et al. ([1], [2]) it is shown how a seafloor shape can be estimated from side-scan sonar data by inversion and regularization. In this paper their method is extended to allow arbitrary vehicle motion. The results of the work will be presented in examples of synthetic and real data.

Deep-sea seafloor shape reconstruction from side-scan sonar data for AUV navigation

Fusing multiple sensors is vital for the precise online localization of mobile robots. The Extended Kalman filter is well suited for this purpose and allows to directly integrate multiple absolute measurements in the filter. However, multiple relative state measurements cannot be fused directly into the filter because they measure differences between a past state and the current state, which introduces correlations. These interdependencies can be modeled by stochastic cloning, which introduces a state augmentation by cloning the respective state estimates connected by a relative measurement. This paper investigates the impact of multiple mixed relative and absolute updates on the estimated state. An approach to feed back information from absolute updates to the cloned state by explicit cloning is presented and compared to existing implicit methods.

Stochastic Cloning for Robust Fusion of Multiple Relative and Absolute Measurements

Navigation of a deep-sea autonomous underwater vehicle (AUV) is a difficult task in cases where no absolute positioning information (e.g., by long baseline systems) is available. Using inertial measurement sensors alone leads to accumulation of sensor noise and with it to corruption of position and orientation estimates over time. Simultaneous localization and mapping (SLAM) techniques can aid to limit the navigation error. In this paper it is discussed what type of features is suitable for sonar-based underwater navigation. For evaluation, a simulation environment has been created which performs environment shape reconstruction based on side-scan sonar data while additionally utilizing vehicular inertial measurement data (ego-motion). A ray-tracing based forward model creates a synthetic sonar response for a given elevation map and vehicle pose. Then, a pose-aware inversion method estimates an elevation map of the surrounding environment. This map is the basis for navigation. Therefore, the characteristics of the reconstruction method need to be taken into account for the feature design as well as the fact that the surface will be sampled differently upon a re-visit.

Philipp Woock

Papers

Deep-sea AUV navigation using side-scan sonar images and SLAM

Odometry-Based Structure from Motion

Deep-sea seafloor shape reconstruction from side-scan sonar data for AUV navigation

Stochastic Cloning for Robust Fusion of Multiple Relative and Absolute Measurements

Survey on suitable 3D features for sonar-based underwater navigation