Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras

Visual scene understanding is an important capability that enables robots to purposefully act in their environment. In this paper, we propose a novel approach to object-class segmentation from multiple RGB-D views using deep learning. We train a deep neural network to predict object-class semantics that is consistent from several view points in a semi-supervised way. At test time, the semantics predictions of our network can be fused more consistently in semantic keyframe maps than predictions of a network trained on individual views. We base our network architecture on a recent single-view deep learning approach to RGB and depth fusion for semantic object-class segmentation and enhance it with multi-scale loss minimization. We obtain the camera trajectory using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth annotated frames in order to enforce multi-view consistency during training. At test time, predictions from multiple views are fused into keyframes. We propose and analyze several methods for enforcing multi-view consistency during training and testing. We evaluate the benefit of multi-view consistency training and demonstrate that pooling of deep features and fusion over multiple views outperforms single-view baselines on the NYUDv2 benchmark for semantic segmentation. Our end-to-end trained network achieves state-of-the-art performance on the NYUDv2 dataset in single-view segmentation as well as multi-view semantic fusion.Comment: the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017

A virtual environment to support the distributed design of large made-to-order products

An overview of a virtual design environment (virtual platform) developed as part of the European Commission funded VRShips-ROPAX (VRS) project is presented. The main objectives for the development of the virtual platform are described, followed by the discussion of the techniques chosen to address the objectives, and finally a description of a use-case for the platform. Whilst the focus of the VRS virtual platform was to facilitate the design of ROPAX (roll-on passengers and cargo) vessels, the components within the platform are entirely generic and may be applied to the distributed design of any type of vessel, or other complex made-to-order products

Accurate measurement of absolute experimental inelastic mean free paths and EELS differential cross-sections

Methods are described for measuring accurate absolute experimental inelastic mean free paths and differential cross-sections using DualEELS. The methods remove the effects of surface layers and give the results for the bulk materials. The materials used are VC0.83,TiC0.98,VN0.97and TiN0.88but the method should be applicable to a wide range of materials. The data were taken at 200 keVusing a probe half angle of 29mradand a collection angle of 36mrad. The background can be subtracted from under the ionisation edges, which can then be separated from each other. This is achieved by scaling Hartree-Slater calculated cross-sections to the edges in the atomic regions well above the threshold. The average scaling factors required are 1.00 for the non-metal K-edges and 1.01 for the metal L-edges (with uncertainties of a few per cent). If preliminary measurements of the chromatic effects in the post-specimen lenses are correct, both drop to 0.99. The inelastic mean free path for TiC0.98 was measured as 103.6±0.5 nm compared to the prediction of 126.9 nm based on the widely used Iakoubovskii parameterisation

Spitzer 70 and 160-micron Observations of the COSMOS Field

We present Spitzer 70 and 160 micron observations of the COSMOS Spitzer survey (S-COSMOS). The data processing techniques are discussed for the publicly released products consisting of images and source catalogs. We present accurate 70 and 160 micron source counts of the COSMOS field and find reasonable agreement with measurements in other fields and with model predictions. The previously reported counts for GOODS-North and the extragalactic First Look Survey are updated with the latest calibration, and counts are measured based on the large area SWIRE survey to constrain the bright source counts. We measure an extragalactic confusion noise level of sigma_c = 9.4+/-3.3 mJy (q=5) for the MIPS 160-micron band based on the deep S-COSMOS data and report an updated confusion noise level of sigma_c = 0.35+/-0.15 mJy (q=5) for the MIPS 70-micron band.Comment: Accepted AJ, 15 Aug. 2009. Data available at http://spider.ipac.caltech.edu/staff/frayer/mycosmos/ until released by IRS