Probabilistic sonar scan matching for an AUV

This paper proposes MSISpIC, a probabilistic sonar scan matching algorithm for the localization of an autonomous underwater vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), the robot displacement estimated through dead-reckoning using a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method is an extension of the pIC algorithm. An extended Kalman filter (EKF) is used to estimate the robot-path during the scan in order to reference all the range and bearing measurements as well as their uncertainty to a scan fixed frame before registering. The major contribution consists of experimentally proving that probabilistic sonar scan matching techniques have the potential to improve the DVL-based navigation. The algorithm has been tested on an AUV guided along a 600 m path within an abandoned marina underwater environment with satisfactory results

Dam wall detection and tracking using a Mechanically Scanned Imaging Sonar

In dam inspection tasks, an underwater robot has to grab images while surveying the wall meanwhile maintaining a certain distance and relative orientation. This paper proposes the use of an MSIS (mechanically scanned imaging sonar) for relative positioning of a robot with respect to the wall. An imaging sonar gathers polar image scans from which depth images (range & bearing) are generated. Depth scans are first processed to extract a line corresponding to the wall (with the Hough transform), which is then tracked by means of an EKF (Extended Kalman Filter) using a static motion model and an implicit measurement equation associating the sensed points to the candidate line. The line estimate is referenced to the robot fixed frame and represented in polar coordinates (rho&thetas) which directly corresponds to the actual distance and relative orientation of the robot with respect to the wall. The proposed system has been tested in simulation as well as in water tank conditions

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

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 a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

SLAM using an Imaging Sonar for Partially Structured Underwater Environments

In this paper we describe a system for underwater navigation with AUVs in partially structured environments, such as dams, ports or marine platforms. An imaging sonar is used to obtain information about the location of planar structures present in such environments. This information is incorporated into a feature-based SLAM algorithm in a two step process: (I) the full 360deg sonar scan is undistorted (to compensate for vehicle motion), thresholded and segmented to determine which measurements correspond to planar environment features and which should be ignored; and (2) SLAM proceeds once the data association is obtained: both the vehicle motion and the measurements whose correct association has been previously determined are incorporated in the SLAM algorithm. This two step delayed SLAM process allows to robustly determine the feature and vehicle locations in the presence of large amounts of spurious or unrelated measurements that might correspond to boats, rocks, etc. Preliminary experiments show the viability of the proposed approach

Integració d'un sensor sonar d'escombrat lateral en un robot submarí

El grup de Visió per Computador i Robòtica (VICOROB) disposa de varis robots submarins per a la recerca i inspecció subaquàtica. Recentment s’ha adquirit un sensor sonar d’escombrat lateral el qual s’utilitza per realitzar imatges acústiques del fons marí quan aquest es mou principalment a velocitat constant i mantenint el rumb. Els robots del grup VICOROB estan equipats amb diferents tipus de sensors i càmeres per analitzar el fons marí. Aquest sensors són de gran qualitat i permeten conèixer de manera bastant satisfactòria l’entorn a les proximitats del robot. Freqüentment però, aquest sensors estant sotmesos a diferents restriccions depenent de la seva naturalesa de funcionament, de tal manera que es necessària la seva combinació per resoldre determinats problemes en diferents situacions. Amb aquest projecte, es pretén integrar un nou sistema de captura d’imatges sonores del fons marí, en un dels robots. Amb la integració d’aquest nou sensor, s’espera obtenir una opció alternativa els sistemes actuals que pugui aportar informació addicional sobre el fons. Aquest sistema podrà ser utilitzat per realitzar tasques per les quals els altres sensors no estant preparats o bé per complementar informació d’altres sensor

Underwater slam for estructured environments using and imaging sonar

Aquesta tesi tracta sobre el problema de la navegació per a vehicles submarins autònoms que operen en entorns artificials estructurats com ara ports, canals, plataformes marines i altres escenaris similars. A partir d'una estimació precisa de la posició en aquests entorns, les capacitats dels vehicles submarins s'incrementen notablement i s'obre una porta al seu funcionament autònom. El manteniment, inspecció i vigilància d'instal lacions marines són alguns exemples de possibles aplicacions. Les principals contribucions d'aquesta tesi consisteixen per una banda en el desenvolupament de diferents sistemes de localització per a aquelles situacions on es disposa d'un mapa previ de l'entorn i per l'altra en el desenvolupament d'una nova solució al problema de la Localització i Construcció Simultània de Mapes (SLAM en les seves sigles en anglès), la finalitat del qual és fer que un vehicle autònom creï un mapa de l'entorn desconegut que el rodeja i, al mateix temps, utilitzi aquest mapa per a determinar la seva pròpia posició. S'ha escollit un sonar d'imatges d'escaneig mecànic com a sensor principal per a aquest treball tant pel seu relatiu baix cost com per la seva capacitat per produir una representació detallada de l'entorn. Per altra banda, les particularitats de la seva operació i, especialment, la baixa freqúència a la que es produeixen les mesures, constitueixen els principals inconvenients que s'han hagut d'abordar en les estratègies de localització proposades. Les solucions adoptades per aquests problemes constitueixen una altra contribució d'aquesta tesi. El desenvolupament de vehicles autònoms i el seu ús com a plataformes experimentals és un altre aspecte important d'aquest treball. Experiments portats a terme tant en el laboratori com en escenaris reals d'aplicació han proporcionat les dades necessàries per a provar i avaluar els diferents sistemes de localització proposats.

Integració d’ una unitat inercial i d’ un giroscopi de fibra òptica en un robot submarí i desenvolupament d’ un sistema de posicionament dins una piscina

El Grup de Visió per Computador i Robòtica (VICOROB) del departament d'Electrònica, Informàtica i Automàtica de la Universitat de Girona investiga en el camp de la robòtica submarina. Al CIRS (Centre d’Investigació en Robòtica Submarina), laboratori que forma part del grup VICOROB, el robot submarí Ictineu és la principal eina utilitzada per a desenvolupar els projectes de recerca. Recentment, el CIRS ha adquirit un nou sistema de sensors d' orientació basat en una unitat inercial i un giroscopi de fibra òptica. Aquest projecte pretén realitzar un estudi d' aquests dispositius i integrar-los al robot Ictineu. D' altra banda, aprofitant les característiques d’aquests sensors giroscopics i les mesures d' un sonar ja integrat al robot, es vol desenvolupar un sistema de localització capaç de determinar la posició del robot en el pla horitzontal de la piscina en temps real

Underwater SLAM in a marina environment

This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas or marine platforms. A mechanical scanning imaging sonar is used to obtain information about the location of planar structures present in such environments. A modified version of the Hough transform has been developed to extract line features, together with their uncertainty, from the continuous sonar dataflow. The information obtained is incorporated into a feature-based SLAM algorithm running an Extended Kalman Filter (EKF). Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach

A proposal to estimate the motion of an underwater vehicle through visual mosaicking

This thesis proposes a solution to the problem of estimating the motion of an Unmanned Underwater Vehicle (UUV). Our approach is based on the integration of the incremental measurements which are provided by a vision system. When the vehicle is close to the underwater terrain, it constructs a visual map (so called "mosaic") of the area where the mission takes place while, at the same time, it localizes itself on this map, following the Concurrent Mapping and Localization strategy. The proposed methodology to achieve this goal is based on a feature-based mosaicking algorithm. A down-looking camera is attached to the underwater vehicle. As the vehicle moves, a sequence of images of the sea-floor is acquired by the camera. For every image of the sequence, a set of characteristic features is detected by means of a corner detector. Then, their correspondences are found in the next image of the sequence. Solving the correspondence problem in an accurate and reliable way is a difficult task in computer vision. We consider different alternatives to solve this problem by introducing a detailed analysis of the textural characteristics of the image. This is done in two phases: first comparing different texture operators individually, and next selecting those that best characterize the point/matching pair and using them together to obtain a more robust characterization. Various alternatives are also studied to merge the information provided by the individual texture operators. Finally, the best approach in terms of robustness and efficiency is proposed. After the correspondences have been solved, for every pair of consecutive images we obtain a list of image features in the first image and their matchings in the next frame. Our aim is now to recover the apparent motion of the camera from these features. Although an accurate texture analysis is devoted to the matching pro-cedure, some false matches (known as outliers) could still appear among the right correspon-dences. For this reason, a robust estimation technique is used to estimate the planar transformation (homography) which explains the dominant motion of the image. Next, this homography is used to warp the processed image to the common mosaic frame, constructing a composite image formed by every frame of the sequence. With the aim of estimating the position of the vehicle as the mosaic is being constructed, the 3D motion of the vehicle can be computed from the measurements obtained by a sonar altimeter and the incremental motion computed from the homography. Unfortunately, as the mosaic increases in size, image local alignment errors increase the inaccuracies associated to the position of the vehicle. Occasionally, the trajectory described by the vehicle may cross over itself. In this situation new information is available, and the system can readjust the position estimates. Our proposal consists not only in localizing the vehicle, but also in readjusting the trajectory described by the vehicle when crossover information is obtained. This is achieved by implementing an Augmented State Kalman Filter (ASKF). Kalman filtering appears as an adequate framework to deal with position estimates and their associated covariances. Finally, some experimental results are shown. A laboratory setup has been used to analyze and evaluate the accuracy of the mosaicking system. This setup enables a quantitative measurement of the accumulated errors of the mosaics created in the lab. Then, the results obtained from real sea trials using the URIS underwater vehicle are shown.