Sonar attentive underwater navigation in structured environment

One of the fundamental requirements of a persistently Autonomous Underwater Vehicle (AUV) is a robust navigation system. The success of most complex robotic tasks depends on the accuracy of a vehicle’s navigation system. In a basic form, an AUV estimates its position using an on-board navigation sensors through Dead-Reckoning (DR). However DR navigation systems tends to drift in the long run due to accumulated measurement errors. One way of mitigating this problem require the use of Simultaneous Localization and Mapping (SLAM) by concurrently mapping external environment features. The performance of a SLAM navigation system depends on the availability of enough good features in the environment. On the contrary, a typical underwater structured environment (harbour, pier or oilfield) has a limited amount of sonar features in a limited locations, hence exploitation of good features is a key for effective underwater SLAM. This thesis develops a novel attentive sonar line feature based SLAM framework that improves the performance of a SLAM navigation by steering a multibeam sonar sensor,which is mounted on a pan and tilt unit, towards feature-rich regions of the environment. A sonar salience map is generated at each vehicle pose to identify highly informative and stable regions of the environment. Results from a simulated test and real AUV experiment show an attentive SLAM performs better than a passive counterpart by repeatedly visiting good sonar landmarks

Feature-relative real-time obstacle avoidance and mapping

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 59-60).A substantial challenge in robotics is integration of complex software systems for real- time performance. This thesis integrates the robust and generic mapping framework Atlas, a feature-based local Simultaneous Localization and Mapping (SLAM) module, and obstacle avoidance using information from mapped features. The resulting system performs autonomous feature-relative Real-time Obstacle Avoidance and Mapping (ROAM) with laser or sonar range sensors, and results are shown for wide-beam sonar. This system will allow high-speed feature-relative obstacle and avoidance and navigation on mobile robots with wide-beam sonar and/or laser sensors.by Jacques Chadwick Leedekerken.M.Eng.and S.B

AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

Geocoder: An Efficient Backscatter Map Constructor

The acoustic backscatter acquired by multibeam and sidescan sonars carries important information about the seafloor morphology and physical properties, providing valuable data to aid the difficult task of seafloor characterization, and important auxiliary information for a bathymetric survey. One necessary step towards this characterization is the assemblage of more consistent and more accurate mosaics of acoustic backscatter. For that, it is necessary to radiometrically correct the backscatter intensities registered by these sonars, to geometrically correct and position each acoustic sample in a projection coordinate system and to interpolate properly the intensity values into a final backscatter map. Geocoder is a software tool that implements the ideas discussed above. Initially, the original backscatter time series registered by the sonar is corrected for angle varying gains, for beam pattern and filtered for speckle removal. All samples of the time series are preserved during all the operations, ensuring that the full data resolution is used for the final mosaicking. The time serie s is then slant-range corrected based on a bathymetric model, in the case of sidescan, or based on beam bathymetry, in the case of the multibeam. Subsequently, each backscatter sample of the series is geocoded in a projected coordinate system in accordance to an interpolation scheme that resembles the acquisition geometry. An anti-aliasing algorithm is applied in parallel to the mosaicking procedure, which allows the assemblage of mosaics at any required resolution. Overlap among parallel lines is resolved by a priority table based on the distance of each sample from the ship track; a blending algorithm is applied to minimize the seams between overlapping lines. The final mosaic exhibits low noise, few artifacts, reduced seams between parallel acquisition lines and reduced clutter in the near-nadir region, while still preserving regional data continuity and local seafloor features

Detecting fish aggregations from reef habitats mapped with high resolution side scan sonar imagery

As part of a multibeam and side scan sonar (SSS) benthic survey of the Marine Conservation District (MCD) south of St. Thomas, USVI and the seasonal closed areas in St. Croix—Lang Bank (LB) for red hind (Epinephelus guttatus) and the Mutton Snapper (MS) (Lutjanus analis) area—we extracted signals from water column targets that represent individual and aggregated fish over various benthic habitats encountered in the SSS imagery. The survey covered a total of 18 km2 throughout the federal jurisdiction fishery management areas. The complementary set of 28 habitat classification digital maps covered a total of 5,462.3 ha; MCDW (West) accounted for 45% of that area, and MCDE (East) 26%, LB 17%, and MS the remaining 13%. With the exception of MS, corals and gorgonians on consolidated habitats were significantly more abundant than submerged aquatic vegetation (SAV) on unconsolidated sediments or unconsolidated sediments. Continuous coral habitat was the most abundant consolidated habitat for both MCDW and MCDE (41% and 43% respectively). Consolidated habitats in LB and MS predominantly consisted of gorgonian plain habitat with 95% and 83% respectively. Coral limestone habitat was more abundant than coral patch habitat; it was found near the shelf break in MS, MCDW, and MCDE. Coral limestone and coral patch habitats only covered LB minimally. The high spatial resolution (0.15 m) of the acquired imagery allowed the detection of differing fish aggregation (FA) types. The largest FA densities were located at MCDW and MCDE over coral communities that occupy up to 70% of the bottom cover. Counts of unidentified swimming objects (USOs), likely representing individual fish, were similar among locations and occurred primarily over sand and shelf edge areas. Fish aggregation school sizes were significantly smaller at MS than the other three locations (MCDW, MCDE, and LB). This study shows the advantages of utilizing SSS in determining fish distributions and density

Establishing a Multibeam Sonar Evaluation Test Bed near Sidney, British Columbia

The Canadian Hydrographic Service (CHS), Naval Oceanographic Office (NAVOCEANO) and the Ocean Mapping Group of the University of New Brunswick (OMG) collaborated on establishing a multibeam sonar test bed in the vicinity of the Institute of Ocean Sciences in Sidney, British Columbia Canada. This paper describes the purpose of the sonar evaluation test bed, the trials and tribulations of two foreign governments collaborating on projects of mutual interest, the evaluation areas and their characteristics for sonar testing, and sample results of sonar evaluations using this test bed. Some target detection comparisons of several systems over a range of artificial sonar targets will also be given

An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization -- one of the main problems affecting other packages in underwater domain -- by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness

Shallow Surveying in Hazardous Waters

Of order one importance to any study of nearshore processes is knowledge of the bathymetry in shallow water. This is true for studies on open coast sandy beaches where surf zone dynamics drive the system, inlet environments where bathymetric evolution can rapidly change navigation channels, and in more benign, lower-energy coastal environments that evolve slowly over 10’s to 100’s of years. Difficulties in obtaining shallow bathymetry where depth-limited wave breaking occurs, submerged hazards are present, or other harsh environments has led to the development of survey systems on highly maneuverable personal watercraft (Beach, et al., 1994; Cote, 1999; Dugan, et al., 1999; MacMahan, 2001). In this work we discuss shallow water surveying from the Coastal Bathymetry Survey System (CBASS), a Yamaha Waverunner equipped with differential GPS, single-beam 192 KHz acoustic echo-sounder, and onboard navigation system. Data obtained with the CBASS in three regions will be discussed, including an energetic surf zone located in southern California during the 2003 Nearshore Canyon Experiment (NCEX), on Lake Erie in 2002 (and compared with historical surveys dating back 150 years), and around Piscataqua River Inlet, NH, in 2007. Estimated accuracy (for sandy bottoms) in water depths ranging 1–10 m are 0.07-0.10 m in the vertical, and on the order of 0.1-1 m horizontally depending on water depth and bottom slope. The high maneuverability of the personal watercraft makes very shallow water bathymetric surveys possible with acoustic altimeters, particularly in regions where airborne remote sensing systems fail (owing to water clarity issues) or where repeated high resolution surveys are required (e.g., where an erodible bottom is rapidly evolving)

Autonomous navigation with constrained consistency for C-Ranger

Autonomous underwater vehicles (AUVs) have become the most widely used tools for undertaking complex exploration tasks in marine environments. Their synthetic ability to carry out localization autonomously and build an environmental map concurrently, in other words, simultaneous localization and mapping (SLAM), are considered to be pivotal requirements for AUVs to have truly autonomous navigation. However, the consistency problem of the SLAM system has been greatly ignored during the past decades. In this paper, a consistency constrained extended Kalman filter (EKF) SLAM algorithm, applying the idea of local consistency, is proposed and applied to the autonomous navigation of the C-Ranger AUV, which is developed as our experimental platform. The concept of local consistency (LC) is introduced after an explicit theoretical derivation of the EKF-SLAM system. Then, we present a locally consistency-constrained EKF-SLAM design, LC-EKF, in which the landmark estimates used for linearization are fixed at the beginning of each local time period, rather than evaluated at the latest landmark estimates. Finally, our proposed LC-EKF algorithm is experimentally verified, both in simulations and sea trials. The experimental results show that the LC-EKF performs well with regard to consistency, accuracy and computational efficiency