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

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

Qualitative design and implementation of human-robot spatial interactions

Despite the large number of navigation algorithms available for mobile robots, in many social contexts they often exhibit inopportune motion behaviours in proximity of people, often with very "unnatural" movements due to the execution of segmented trajectories or the sudden activation of safety mechanisms (e.g., for obstacle avoidance). We argue that the reason of the problem is not only the difficulty of modelling human behaviours and generating opportune robot control policies, but also the way human-robot spatial interactions are represented and implemented. In this paper we propose a new methodology based on a qualitative representation of spatial interactions, which is both flexible and compact, adopting the well-defined and coherent formalization of Qualitative Trajectory Calculus (QTC). We show the potential of a QTC-based approach to abstract and design complex robot behaviours, where the desired robot's behaviour is represented together with its actual performance in one coherent approach, focusing on spatial interactions rather than pure navigation problems

Appearance-based localization for mobile robots using digital zoom and visual compass

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally

Understanding images in biological and computer vision

yesThis issue of Interface Focus is a collection of papers arising out of a Royal Society Discussion meeting entitled ‘Understanding images in biological and computer vision’ held at Carlton Terrace on the 19th and 20th February, 2018. There is a strong tradition of inter-disciplinarity in the study of visual perception and visual cognition. Many of the great natural scientists including Newton [1], Young [2] and Maxwell (see [3]) were intrigued by the relationship between light, surfaces and perceived colour considering both physical and perceptual processes. Brewster [4] invented both the lenticular stereoscope and the binocular camera but also studied the perception of shape-from-shading. More recently, Marr's [5] description of visual perception as an information processing problem led to great advances in our understanding of both biological and computer vision: both the computer vision and biological vision communities have a Marr medal. The recent successes of deep neural networks in classifying the images that we see and the fMRI images that reveal the activity in our brains during the act of seeing are both intriguing. The links between machine vision systems and biology may at sometimes be weak but the similarity of some of the operations is nonetheless striking [6]. This two-day meeting brought together researchers from the fields of biological and computer vision, robotics, neuroscience, computer science and psychology to discuss the most recent developments in the field. The meeting was divided into four themes: vision for action, visual appearance, vision for recognition and machine learning

tuning optical properties of opal photonic crystals by structural defects engineering

We report on the preparation and optical characterization of three dimensional colloidal photonic crystal (PhC) containing an engineered planar defect embedding photoactive push-pull dyes. Free standing polystyrene films having thickness between 0.6 and 3 microns doped with different dipolar chromophores were prepared. These films were sandwiched between two artificial opals creating a PhC structure with planar defect. The system was characterized by reflectance at normal incidence angle (R), variable angle transmittance (T), and photoluminescence spectroscopy (PL). Clear evidence of defect states were observed in T and R spectra, which allow the light to propagate for selected frequencies within the pseudogap (stop band)

Robot Perception of Static and Dynamic Objects with an Autonomous Floor Scrubber

This paper presents the perception system of a new professional cleaning robot for large public places. The proposed system is based on multiple sensors including 3D and 2D lidar, two RGB-D cameras and a stereo camera. The two lidars together with an RGB-D camera are used for dynamic object (human) detection and tracking, while the second RGB-D and stereo camera are used for detection of static objects (dirt and ground objects). A learning and reasoning module for spatial-temporal representation of the environment based on the perception pipeline is also introduced. Furthermore, a new dataset collected with the robot in several public places, including a supermarket, a warehouse and an airport, is released.Baseline results on this dataset for further research and comparison are provided. The proposed system has been fully implemented into the Robot Operating System (ROS) with high modularity, also publicly available to the community

RESULTADOS PRELIMINARES DA DISTRIBUIÇÃO DE FÓSFORO E SUAS FORMAS NOS SEDIMENTOS DA PLATAFORMA CONTINENTAL DO ESTADO DE SANTA CATARINA

Distribuition of phosphorus in marine sediments have been recognized as a useful tool for the knowledgement of its geochemical cicle, that have been undergoing chances by human actions. Marine sediments represents the major reservoir of this constituint, where processes take place and control its disponibility to water column. Differents forms of phosphorus were determined by sequencial extrations in 27 samples of bottom sediment collected in the inner shelf off Santa Catarina. The aim of this study was to investigate the distribuition of phosphorus in the sequencial extrated frations and the relationship with sediment facies. The highest total phosphorus concentrations have been related to fine grained sediments. Although, there were some differences between samples with similar sedimentar patterns. Iron plus aluminium phosphate has been showed high concentrations in the neigborhood of river mounths, declining off shore. Calcium phosphate was the predominant form in most samples.O estudo de fósforo em sedimentos marinhos tem sido utilizado como importante ferramenta para compreensão do ciclo geoquímico deste nutriente, o qual vem sendo alterado pela ação antropogênica. Os sedimentos marinhos representam o depósito final deste constituinte e, dependendo dos processos de interação química que ali ocorrem, podem ser responsáveis por sua maior ou menor disponibilidade para a coluna d’água. Neste estudo foram determinadas as diferentes formas de fósforo, através do método de extração sequencial (modificado de SILVEIRA, 1993), em 27 amostras de sedimentos superficiais coletados na plataforma interna do litoral centro-norte catarinense. O objetivo era verificar a distribuição de fósforo e suas formas de acordo com as fácies sedimentares encontradas na área de estudo. As concentrações de fósforo total variaram entre 1,27 a 18,77 µmol/g, com as mais elevadas estando associadas aos sedimentos finos, embora tenham ocorrido diferenças entre fácies com características granolumétricas similares. O fosfato de ferro + alumínio apresentou concentrações mais altas próximo as desembocaduras dos rios diminuindo com o aumento da distância da linha de costa, indicando o aporte continental. A forma predominante na maioria das amostras foi o fosfato de cálcio que apresentou uma relação direta com a profundidade

Online Learning for 3D LiDAR-based Human Detection: Experimental Analysis of Point Cloud Clustering and Classification Methods

This paper presents a system for online learning of human classifiers by mobile service robots using 3D~LiDAR sensors, and its experimental evaluation in a large indoor public space. The learning framework requires a minimal set of labelled samples (e.g. one or several samples) to initialise a classifier. The classifier is then retrained iteratively during operation of the robot. New training samples are generated automatically using multi-target tracking and a pair of "experts" to estimate false negatives and false positives. Both classification and tracking utilise an efficient real-time clustering algorithm for segmentation of 3D point cloud data. We also introduce a new feature to improve human classification in sparse, long-range point clouds. We provide an extensive evaluation of our the framework using a 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments demonstrate the influence of the system components and improved classification of humans compared to the state-of-the-art