Augmented state Kalman filtering for AUV navigation

This paper addresses the problem of estimating the motion of an Autonomous Underwater Vehicle (AUV), while it constructs a visual map (“mosaic ” image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position.

USE OF DECISION TREES IN COLOUR FEATURE SELECTION. APPLICATION TO OBJECT RECOGNITION IN OUTDOOR SCENES

ABSTRACT A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several ndimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discriminate power for each concrete subset of features is performed by means of decision trees composed of linear discriminate functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported

Globally aligned photomosaic of the Lucky Strike hydrothermal vent field (Mid-Atlantic Ridge, 37°18.5′N) : release of georeferenced data, mosaic construction, and viewing software

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry Geophysics Geosystems 9 (2008): Q12009, doi:10.1029/2008GC002204.We present a georeferenced photomosaic of the Lucky Strike hydrothermal vent field (Mid-Atlantic Ridge, 37°18′N). The photomosaic was generated from digital photographs acquired using the ARGO II seafloor imaging system during the 1996 LUSTRE cruise, which surveyed a ∼1 km2 zone and provided a coverage of ∼20% of the seafloor. The photomosaic has a pixel resolution of 15 mm and encloses the areas with known active hydrothermal venting. The final mosaic is generated after an optimization that includes the automatic detection of the same benthic features across different images (feature-matching), followed by a global alignment of images based on the vehicle navigation. We also provide software to construct mosaics from large sets of images for which georeferencing information exists (location, attitude, and altitude per image), to visualize them, and to extract data. Georeferencing information can be provided by the raw navigation data (collected during the survey) or result from the optimization obtained from image matching. Mosaics based solely on navigation can be readily generated by any user but the optimization and global alignment of the mosaic requires a case-by-case approach for which no universally software is available. The Lucky Strike photomosaics (optimized and navigated-only) are publicly available through the Marine Geoscience Data System (MGDS, http://www.marine-geo.org). The mosaic-generating and viewing software is available through the Computer Vision and Robotics Group Web page at the University of Girona (http://eia.udg.es/∼rafa/mosaicviewer.html).This work has been supported by the EU Marie Curie RTNs MOMARNet (OD, RG, JE, LN, JF, NG) and FREESUBNet (RG, NG, XC), the Spanish Ministry of Science and Innovation (grant CTM2007–64751; RG, JE), CNRS and ANR (grant ANR NT05–3_42212, JE), ICREA (LN), and by the Generalitat de Catalunya (JE, RG). JF has been funded by MICINN under FPI grant BES-2006-12733 and NG has been supported by MICINN under the ‘‘Ramon y Cajal’’ program

Results of the ontology alignment evaluation initiative 2023

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2023 campaign offered 15 tracks and was attended by 16 participants. This paper is an overall presentation of that campaign

Results of the Ontology Alignment Evaluation Initiative 2023

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2023 campaign offered 15 tracks and was attended by 16 participants. This paper is an overall presentation of that campaign