147 research outputs found
Improving Sonar Image Patch Matching via Deep Learning
Matching sonar images with high accuracy has been a problem for a long time,
as sonar images are inherently hard to model due to reflections, noise and
viewpoint dependence. Autonomous Underwater Vehicles require good sonar image
matching capabilities for tasks such as tracking, simultaneous localization and
mapping (SLAM) and some cases of object detection/recognition. We propose the
use of Convolutional Neural Networks (CNN) to learn a matching function that
can be trained from labeled sonar data, after pre-processing to generate
matching and non-matching pairs. In a dataset of 39K training pairs, we obtain
0.91 Area under the ROC Curve (AUC) for a CNN that outputs a binary
classification matching decision, and 0.89 AUC for another CNN that outputs a
matching score. In comparison, classical keypoint matching methods like SIFT,
SURF, ORB and AKAZE obtain AUC 0.61 to 0.68. Alternative learning methods
obtain similar results, with a Random Forest Classifier obtaining AUC 0.79, and
a Support Vector Machine resulting in AUC 0.66.Comment: Author versio
Information-Preserved Blending Method for Forward-Looking Sonar Mosaicing in Non-Ideal System Configuration
Forward-Looking Sonar (FLS) has started to gain attention in the field of
near-bottom close-range underwater inspection because of its high resolution
and high framerate features. Although Automatic Target Recognition (ATR)
algorithms have been applied tentatively for object-searching tasks, human
supervision is still indispensable, especially when involving critical areas. A
clear FLS mosaic containing all suspicious information is in demand to help
experts deal with tremendous perception data. However, previous work only
considered that FLS is working in an ideal system configuration, which assumes
an appropriate sonar imaging setup and the availability of accurate positioning
data. Without those promises, the intra-frame and inter-frame artifacts will
appear and degrade the quality of the final mosaic by making the information of
interest invisible. In this paper, we propose a novel blending method for FLS
mosaicing which can preserve interested information. A Long-Short Time Sliding
Window (LST-SW) is designed to rectify the local statistics of raw sonar
images. The statistics are then utilized to construct a Global Variance Map
(GVM). The GVM helps to emphasize the useful information contained in images in
the blending phase by classifying the informative and featureless pixels,
thereby enhancing the quality of final mosaic. The method is verified using
data collected in the real environment. The results show that our method can
preserve more details in FLS mosaics for human inspection purposes in practice
Forward-Looking Sonar Patch Matching:Modern CNNs, Ensembling, and Uncertainty
Application of underwater robots are on the rise, most of them are dependent on sonar for underwater vision, but the lack of strong perception capabilities limits them in this task. An important issue in sonar perception is matching image patches, which can enable other techniques like localization, change detection, and mapping. There is a rich literature for this problem in color images, but for acoustic images, it is lacking, due to the physics that produce these images. In this paper we improve on our previous results for this problem (Valdenegro-Toro et al, 2017), instead of modeling features manually, a Convolutional Neural Network (CNN) learns a similarity function and predicts if two input sonar images are similar or not. With the objective of improving the sonar image matching problem further, three state of the art CNN architectures are evaluated on the Marine Debris dataset, namely DenseNet, and VGG, with a siamese or two-channel architecture, and contrastive loss. To ensure a fair evaluation of each network, thorough hyper-parameter optimization is executed. We find that the best performing models are DenseNet Two-Channel network with 0.955 AUC, VGG-Siamese with contrastive loss at 0.949 AUC and DenseNet Siamese with 0.921 AUC. By ensembling the top performing DenseNet two-channel and DenseNet-Siamese models overall highest prediction accuracy obtained is 0.978 AUC, showing a large improvement over the 0.91 AUC in the state of the art
Archaeology via underwater robots : mapping and localization within Maltese cistern systems
This paper documents the application of several
underwater robot mapping and localization techniques used
during an archaeological expedition. The goal of this project was
to explore and map ancient cisterns located on the islands of
Malta and Gozo. The cisterns of interest acted as water storage
systems for fortresses, private homes, and churches. They often
consisted of several connected chambers, still containing water. A
sonar-equipped Remotely Operated Vehicle (ROV) was deployed
into these cisterns to obtain both video footage and sonar range
measurements. Four different mapping and localization
techniques were employed including 1) Sonar image mosaics
using stationary sonar scans, and 2) Simultaneous Localization
and Mapping (SLAM) while the vehicle was in motion, 3) SLAM
using stationary sonar scans, and 4) Localization using previously
created maps. Two dimensional maps of 6 different cisterns were
successfully constructed. It is estimated that the cisterns were
built as far back as 300 B.C.peer-reviewe
Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.
This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments.
We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd
Toward autonomous exploration in confined underwater environments
Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 33 (2016): 994-1012, doi:10.1002/rob.21640.In this field note we detail the operations and discuss the results of an experiment conducted
in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic
sonar to simultaneously map the caves’ horizontal and vertical surfaces. Although the
caves’ spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and mapping (SLAM) framework that significantly reduces and bounds the localization error for fully
autonomous navigation. These methods are generalizable for AUV exploration in confined
underwater environments where surfacing or pre-deployment of localization equipment are
not feasible and may provide a useful step toward AUV utilization as a response tool in
confined underwater disaster areas.This research work was partially sponsored by the EU FP7-Projects: Tecniospring-
Marie Curie (TECSPR13-1-0052), MORPH (FP7-ICT-2011-7-288704), Eurofleets2 (FP7-INF-2012-312762),
and the National Science Foundation (OCE-0955674)
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