Collective classification for labeling of places and objects in 2D and 3D range data

In this paper, we present an algorithm to identify types of places and objects from 2D and 3D laser range data obtained in indoor environments. Our approach is a combination of a collective classification method based on associative Markov networks together with an instance-based feature extraction using nearest neighbor. Additionally, we show how to select the best features needed to represent the objects and places, reducing the time needed for the learning and inference steps while maintaining high classification rates. Experimental results in real data demonstrate the effectiveness of our approach in indoor environments

Semantic labeling of places using information extracted from laser and vision sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments

Determination of the relative amounts of Gag and Pol proteins in foamy virus particles

We determined the relative ratios of Gag and Pol molecules in highly purified virions of spumaretroviruses or foamy viruses (FVs) using monoclonal antibodies and bacterially expressed reference proteins. We found that the cleaved p68(Gag )moiety dominates in infectious FVs. Furthermore, approximate mean ratios in FV are 16:1 (pr71(Gag )plus p68(Gag):p85(RT)),12:1 (p68(Gag):p85(RT)), and 10:1 (pr71(Gag )plus p68(Gag):p40(IN)). Thus, the results indicate that FVs have found a way to incorporate approximately as much Pol protein into their capsids as orthoretroviruses, despite a completely different Pol expression strategy

Using Hierarchical EM to Extract Planes from 3D Range Scans

©2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2005 IEEE International Conference on Robotics and Automation (ICRA), 18-22 April 2005, Barcelona, Spain.DOI: 10.1109/ROBOT.2005.1570803Recently, the acquisition of three-dimensional maps has become more and more popular. This is motivated by the fact that robots act in the three-dimensional world and several tasks such as path planning or localizing objects can be carried out more reliable using three-dimensional representations. In this paper we consider the problem of extracting planes from three-dimensional range data. In contrast to previous approaches our algorithm uses a hierarchical variant of the popular Expectation Maximization (EM) algorithm [1] to simultaneously learn the main directions of the planar structures. These main directions are then used to correct the position and orientation of planes. In practical experiments carried out with real data and in simulations we demonstrate that our algorithm can accurately extract planes and their orientation from range data

Supervised semantic labeling of places using information extracted from sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments

GeoLaB – Geothermal Laboratory in the crystalline Basement: synergies with research for a nuclear waste repository

Crystalline rocks are being considered as potential host rocks in the ongoing search for a suitable site for a nuclear waste repository in Germany, where there is no existing experience in terms of excavating a repository in crystalline rocks. The planned underground laboratory GeoLaB addressing crystalline geothermal reservoirs offers unique opportunities for synergies with nuclear waste disposal research and development, especially in the exploration and building phases

AZT-resistant foamy virus

AbstractAzidothymidine (AZT) is a reverse transcriptase (RT) inhibitor that efficiently blocks the replication of spumaretroviruses or foamy viruses (FVs). To more precisely elucidate the mechanism of action of the FV RT enzyme, we generated an AZT-resistant FV in cell culture. Biologically resistant virus was obtained for simian foamy virus from macaque (SFVmac), which was insensitive to AZT concentrations of 1 mM, but not for FVs derived from chimpanzees. Nucleotide sequencing revealed four non-silent mutations in the pol gene. Introduction of these mutations into an infectious molecular clone identified all changes to be required for the fully AZT-resistant phenotype of SFVmac. The alteration of individual sites showed that AZT resistance in SFVmac was likely acquired by consecutive acquisition of pol mutations in a defined order, because some alterations on their own did not result in an efficiently replicating virus, neither in the presence nor in the absence of AZT. The introduction of the mutations into the RT of the closely related prototypic FV (PFV) did not yield an AZT-resistant virus, instead they significantly impaired the viral fitness

Characterisation of Australian MRSA Strains ST75- and ST883-MRSA-IV and Analysis of Their Accessory Gene Regulator Locus

Background: Community-acquired methicillin-resistant Staphylococcus aureus have become a major problem in Australia. These strains have now been isolated throughout Australia including remote Indigenous communities that have had minimal exposure to healthcare facilities. Some of these strains, belonging to sequence types ST75 and ST883, have previously been reported to harbour highly divergent alleles of the housekeeping genes used in multilocus sequence typing. Methodology/Principal Findings: ST75-MRSA-IV and ST883-MRSA-IV isolates were characterised in detail. Morphological features as well as 16S sequences were identical to other S. aureus strains. Although a partial rnpB gene sequence was not identical to previously known S. aureus sequences, it was found to be more closely related to S. aureus than to other staphylococci. Isolates also were screened using diagnostic DNA microarrays. These isolates yielded hybridisation results atypical for S. aureus. Primer directed amplification assays failed to detect species markers (femA, katA, sbi, spa). However, arbitrarily primed amplification indicated the presence of unknown alleles of these genes. Isolates could not be assigned to capsule types 1, 5 or 8. The allelic group of the accessory gene regulator (agr) locus was not determinable. Sequencing of a region of agrB, agrC and agrD (approximately 2,100 bp) revealed a divergent sequence. However, this sequence is more related to S. aureus agr alleles I and IV than to agr sequences from other Staphylococcus species. The predicted autoinducing peptide (AIP) sequence of ST75 was identical to that of agr group I, while the predicted AIP sequence of ST883 was identical to agr group IV. Conclusions/Significance: The genetic properties of ST75/ST883-MRSA may be due to a series of evolutionary events in ancient insulated S. aureus strains including a convergent evolution leading to agr group I- or IV-like AIP sequences and a recent acquisition of SCCmec IV elements