1,819 research outputs found
Autonomous navigation for guide following in crowded indoor environments
The requirements for assisted living are rapidly changing as the number of elderly
patients over the age of 60 continues to increase. This rise places a high level of stress on
nurse practitioners who must care for more patients than they are capable. As this trend is
expected to continue, new technology will be required to help care for patients. Mobile
robots present an opportunity to help alleviate the stress on nurse practitioners by
monitoring and performing remedial tasks for elderly patients. In order to produce
mobile robots with the ability to perform these tasks, however, many challenges must be
overcome.
The hospital environment requires a high level of safety to prevent patient injury. Any
facility that uses mobile robots, therefore, must be able to ensure that no harm will come
to patients whilst in a care environment. This requires the robot to build a high level of
understanding about the environment and the people with close proximity to the robot.
Hitherto, most mobile robots have used vision-based sensors or 2D laser range finders.
3D time-of-flight sensors have recently been introduced and provide dense 3D point
clouds of the environment at real-time frame rates. This provides mobile robots with
previously unavailable dense information in real-time. I investigate the use of time-of-flight
cameras for mobile robot navigation in crowded environments in this thesis. A
unified framework to allow the robot to follow a guide through an indoor environment
safely and efficiently is presented. Each component of the framework is analyzed in
detail, with real-world scenarios illustrating its practical use.
Time-of-flight cameras are relatively new sensors and, therefore, have inherent problems
that must be overcome to receive consistent and accurate data. I propose a novel and
practical probabilistic framework to overcome many of the inherent problems in this
thesis. The framework fuses multiple depth maps with color information forming a
reliable and consistent view of the world. In order for the robot to interact with the
environment, contextual information is required. To this end, I propose a region-growing
segmentation algorithm to group points based on surface characteristics, surface normal
and surface curvature. The segmentation process creates a distinct set of surfaces,
however, only a limited amount of contextual information is available to allow for
interaction. Therefore, a novel classifier is proposed using spherical harmonics to
differentiate people from all other objects.
The added ability to identify people allows the robot to find potential candidates to
follow. However, for safe navigation, the robot must continuously track all visible
objects to obtain positional and velocity information. A multi-object tracking system is
investigated to track visible objects reliably using multiple cues, shape and color. The
tracking system allows the robot to react to the dynamic nature of people by building an
estimate of the motion flow. This flow provides the robot with the necessary information
to determine where and at what speeds it is safe to drive. In addition, a novel search
strategy is proposed to allow the robot to recover a guide who has left the field-of-view.
To achieve this, a search map is constructed with areas of the environment ranked
according to how likely they are to reveal the guideās true location. Then, the robot can
approach the most likely search area to recover the guide. Finally, all components
presented are joined to follow a guide through an indoor environment. The results
achieved demonstrate the efficacy of the proposed components
STV-based Video Feature Processing for Action Recognition
In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end
Enriching the fan experience in a smart stadium using internet of things technologies
Rapid urbanization has brought about an influx of people to cities, tipping the scale between urban and rural living. Population predictions estimate that 64% of the global population will reside in cities by 2050. To meet the growing resource needs, improve management, reduce complexities, and eliminate unnecessary costs while enhancing the quality of life of citizens, cities are increasingly exploring open innovation frameworks and smart city initiatives that target priority areas including transportation, sustainability, and security. The size and heterogeneity of urban centers impede progress of technological innovations for smart cities. We propose a Smart Stadium as a living laboratory to balance both size and heterogeneity so that smart city solutions and Internet of Things (IoT) technologies may be deployed and tested within an environment small enough to practically trial but large and diverse enough to evaluate scalability and efficacy. The Smart Stadium for Smart Living initiative brings together multiple institutions and partners including Arizona State University (ASU), Dublin City University (DCU), Intel Corporation, and Gaelic Athletic Association (GAA), to turn ASU's Sun Devil Stadium and Ireland's Croke Park Stadium into twinned smart stadia to investigate IoT and smart city technologies and applications
A Person Following Algorithm for Use with a Single Forward Facing RGB-D Camera on a Mobile Robot
This thesis examines the problem of person following. A person following algorithm can be separated into two distinct parts: the detection and tracking of a target and the actual following of a target. This thesis focuses mainly on the detection and tracking of a target person. For the purposes of this thesis a simple robot control architecture is used. The robot moves to follow the target in a straight line. No path planning is considered when executing robot movement. This thesis aims to accomplish three tasks. First, the system should be able to track and follow a target when no occlusions occur. The non-occlusion scenarios should consider the target in environments with no other people, environments with other people present at different distances, and environments with other people present at similar distances. The second goal will be to track the target person through brief occlusions. The system should be able to detect when the target has been occluded, register the occlusion, and reacquire the target upon completion of the occlusion. The third and final goal of this thesis is to reacquire the target after a long term occlusion. The system must recognize that the target person has disappeared from the scene, wait for the target to reappear, and reacquire the target upon reappearance. These goals will be accomplished using a generic person detector realized by a HOG person detector, a specific appearance model based on color histograms, a particle filter that will serve as an integrating structure for the tracker, and a simplistic robot control architecture. In the following chapters I will discuss the motivation behind this work, previous research done in this area, the methods used in this thesis and the theory behind them. Experimental results will then be analyzed and discussion concerning the results and possible improvements to the system will be presented
- ā¦