A practical multirobot localization system

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems

XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera

We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates successfully in generic scenes which may contain occlusions by objects and by other people. Our method operates in subsequent stages. The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals.We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy. In the second stage, a fully connected neural network turns the possibly partial (on account of occlusion) 2Dpose and 3Dpose features for each subject into a complete 3Dpose estimate per individual. The third stage applies space-time skeletal model fitting to the predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose, and enforce temporal coherence. Our method returns the full skeletal pose in joint angles for each subject. This is a further key distinction from previous work that do not produce joint angle results of a coherent skeleton in real time for multi-person scenes. The proposed system runs on consumer hardware at a previously unseen speed of more than 30 fps given 512x320 images as input while achieving state-of-the-art accuracy, which we will demonstrate on a range of challenging real-world scenes.Comment: To appear in ACM Transactions on Graphics (SIGGRAPH) 202

XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera

We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates in generic scenes and is robust to difficult occlusions both by other people and objects. Our method operates in subsequent stages. The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy. In the second stage, a fully-connected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual. The third stage applies space-time skeletal model fitting to the predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose, and enforce temporal coherence. Our method returns the full skeletal pose in joint angles for each subject. This is a further key distinction from previous work that neither extracted global body positions nor joint angle results of a coherent skeleton in real time for multi-person scenes. The proposed system runs on consumer hardware at a previously unseen speed of more than 30 fps given 512x320 images as input while achieving state-of-the-art accuracy, which we will demonstrate on a range of challenging real-world scenes

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data

Collection of anthropometry from older and physically impaired persons: traditional methods versus TC2 3-D body scanner

With advances in technology it is now possible to collect a wide range of anthropometric data, to a high degree of accuracy, using 3D light-based body scanners. This gives the potential to speed up the collection of anthropometric data for design purposes, to decrease processing time and data input required, and to reduce error due to inaccuracy of measurements taken using more traditional methods and equipment (anthropometer, stadiometer and sitting height table). However, when the data collection concerns older and/or physically impaired people there are serious issues for consideration when deciding on the best method to collect anthropometry. This paper discusses the issues arising when collecting data using both traditional methods of data collection and a first use by the experimental team of the TC2 3D body scanner, when faced with a ‘non-standard’ sample, during an EPSRC funded research project into issues surrounding transport usage by older and physically impaired people. Relevance to industry: Designing products, environments and services so that the increasing ageing population, as well as the physically impaired, can use them increases the potential market. To do this, up-to-date and relevant anthropometry is often needed. 3D light-based bodyscanners offer a potential fast way of obtaining this data, and this paper discusses some of the issues with using one scanner with older and disabled people

Airborne photogrammetry and LIDAR for DSM extraction and 3D change detection over an urban area : a comparative study

A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km(2). The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t(1) and t(2), are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t(2) - t(1). Based on the change model, the surface and volume of the building changes can be quantified

Automatic analysis of transperineal ultrasound images

This thesis focuses on the automatic image analysis of transperineal ultrasound (TPUS) data, which is used to investigate female pelvic floor problems. These problems have a high prevalence, but the understanding of pelvic floor (dys-)function is limited. TPUS analysis of the pelvic floor is done manually, which is time-consuming and observer dependent. This hinders both the research into interpretation of TPUS data and its clinical use. To overcome these problems we use automatic image analysis. Currently, one of the main methods used, to analyse the TPUS is manually selecting and segmenting the slice of minimal hiatal dimensions (SMHD). In the first chapter of this thesis we show that reliable automatic segmentation of the urogenital hiatus and the puborectalis muscle in the SMHD can be successfully implemented, using deep learning. Furthermore, we show that this can also be used to successfully automate the process of selecting and segmenting the SMHD. 4D TPUS is available in the clinical practice but by the aforementioned method only provides 1D and 2D parameters. Therefore, information stored within TPUS about the volume appearance of the pelvic floor muscles and muscle functionality is not analyzed. In the third chapter of this thesis we propose a reproducible manual 3D segmentation protocol of the puborectalis muscle. The resulting manual segmentations can be used to train active appearance models and convolutional neural networks, these algorithms can be used for reliable automatic 3D segmentation. In the fifth chapter of we show that on this data it is possible to identify all subdivisions of the main pelvic floor muscle group, the levator ani muscles, on new TPUS data. In the last chapter we apply unsupervised deep learning to our data and show that this can be used for classification of the TPUS data. The segmentation results presented in this thesis are an important step to reduce the TPUS analysis time and will therefore ease the study of large populations and clinical TPUS analysis. The 3D identification and segmentation of the levator ani muscle subdivisions helps to identify if they are still intact. This is an important step to better informed clinical decision-making