A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

Learning sound representations using trainable COPE feature extractors

Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR). In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined using a single prototype sound pattern in an automatic configuration process, which is a type of representation learning. We construct a set of COPE feature extractors, configured on a number of training patterns. Then we take their responses to build feature vectors that we use in combination with a classifier to detect and classify patterns of interest in audio signals. We carried out experiments on four public data sets: MIVIA audio events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund) demonstrate the effectiveness of the proposed method and are higher than the ones obtained by other existing approaches. The COPE feature extractors have high robustness to variations of SNR. Real-time performance is achieved even when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio

Hercules finances research infrastructure

In 2007 the Flemish government created a structural funding channel to support investment in research infrastructure: Hercules. On 15 October 2008 the Hercules Foundation approved a first list of investment proposals. In this article specific features of this first call are examined

Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles

El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos. Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap). La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Investigation of resin systems for improved ablative materials Final report, 19 Jun. 1964 - 31 Jul. 1965

Resin systems investigated for improving ablative materials for use with fluorine-containing liquid propellant system

Design and conduct of 'Xtreme Alps' : a double-blind, randomised controlled study of the effects of dietary nitrate supplementation on acclimatisation to high altitude

The study of healthy human volunteers ascending to high altitude provides a robust model of the complex physiological interplay that emulates human adaptation to hypoxaemia in clinical conditions. Nitric oxide (NO) metabolism may play an important role in both adaptation to high altitude and response to hypoxaemia during critical illness at sea level. Circulating nitrate and nitrite concentrations can be augmented by dietary supplementation and this is associated with improved exercise performance and mitochondrial efficiency. We hypothesised that the administration of a dietary substance (beetroot juice) rich in nitrate would improve oxygen efficiency during exercise at high altitude by enhancing tissue microcirculatory blood flow and oxygenation. Furthermore, nitrate supplementation would lead to measurable increases in NO bioactivity throughout the body. This methodological manuscript describes the design and conduct of the ‘Xtreme Alps’ expedition, a double-blind randomised controlled trial investigating the effects of dietary nitrate supplementation on acclimatisation to hypobaric hypoxia at high altitude in healthy human volunteers. The primary outcome measure was the change in oxygen efficiency during exercise at high altitude between participants allocated to receive nitrate supplementation and those receiving a placebo. A number of secondary measures were recorded, including exercise capacity, peripheral and microcirculatory blood flow and tissue oxygenation. Results from this study will further elucidate the role of NO in adaption to hypoxaemia and guide clinical trials in critically ill patients. Improved understanding of hypoxaemia in critical illness may provide new therapeutic avenues for interventions that will improve survival in critically ill patients

Measuring an orienteer's performance: a method based on lower rank approximation of asymmetric matrices

The objective of this thesis is to develop a method that measures the performance of competitors in an orienteering course. The measures proposed will allow the comparison of the orienteer’s performance with other competitors running the same course, and with his/her performance in different courses. This thesis presents two main contributions, the first one refers to an orienteer’s performance measure, and the second one relates to the lower rank approximation theory developed for this research. In the orienteering context, we developed a method that calculates a fitness measure and a navigational measure for each orienteer running a course. This differentiation between physical and navigational skills is an original approach to the orienteering performance analysis, and it allows a clear identification of areas that need improvement to achieve better times in an event. Based on the performance measures of orienteers participating in 100 events that took place in the UK between January 2013 and May 2014, we construct a course technical difficulty measure for green, blue and brown courses. This course difficulty allows the identification of easy and hard courses. On the statistical theory context, this thesis presents a robust and asymmetric lower rank approximation algorithm to reduce matrices with asymmetric outliers into two vectors. This algorithm was based on the robust lower rank approximation algorithm proposed by Maronna and Yohai (2008). This thesis presents two original modifications to this algorithm, which produces better estimates when the data has asymmetric outliers. Those modifications are the definition of an asymmetric objective function, and the use of a robust scale parameter that is updated at each iteration. The proposed modification to the scale parameter caused that the estimates do not depend anymore on initial values and the value reached by the loss function is most of the time better than the point reached without the modification. We also show that this methodology can be applied in contexts other than orienteering