Characterization of image sets: the Galois Lattice approach

This paper presents a new method for supervised image classification. One or several landmarks are attached to each class, with the intention of characterizing it and discriminating it from the other classes. The different features, deduced from image primitives, and their relationships with the sets of images are structured and organized into a hierarchy thanks to an original method relying on a mathematical formalism called Galois (or Concept) Lattices. Such lattices allow us to select features as landmarks of specific classes. This paper details the feature selection process and illustrates this through a robotic example in a structured environment. The class of any image is the room from which the image is shot by the robot camera. In the discussion, we compare this approach with decision trees and we give some issues for future research

Vision-Based Localization Algorithm Based on Landmark Matching, Triangulation, Reconstruction, and Comparison

Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRC) global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data

Conceptual design for Mobile Geological Laboratory position and heading fix system

Conceptual design of position fixing system for Mobile Geological Laboratory in Lunar Mobile Laboratory simulatio

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

Study and simulation results for video landmark acquisition and tracking technology (Vilat-2)

The results of several investigations and hardware developments which supported new technology for Earth feature recognition and classification are described. Data analysis techniques and procedures were developed for processing the Feature Identification and Location Experiment (FILE) data. This experiment was flown in November 1981, on the second Shuttle flight and a second instrument, designed for aircraft flights, was flown over the United States in 1981. Ground tests were performed to provide the basis for designing a more advanced version (four spectral bands) of the FILE which would be capable of classifying clouds and snow (and possibly ice) as distinct features, in addition to the features classified in the Shuttle experiment (two spectral bands). The Shuttle instrument classifies water, bare land, vegetation, and clouds/snow/ice (grouped)

Environmental modeling and recognition for an autonomous land vehicle

An architecture for object modeling and recognition for an autonomous land vehicle is presented. Examples of objects of interest include terrain features, fields, roads, horizon features, trees, etc. The architecture is organized around a set of data bases for generic object models and perceptual structures, temporary memory for the instantiation of object and relational hypotheses, and a long term memory for storing stable hypotheses that are affixed to the terrain representation. Multiple inference processes operate over these databases. Researchers describe these particular components: the perceptual structure database, the grouping processes that operate over this, schemas, and the long term terrain database. A processing example that matches predictions from the long term terrain model to imagery, extracts significant perceptual structures for consideration as potential landmarks, and extracts a relational structure to update the long term terrain database is given

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Effects of spatial ability on multi-robot control tasks

Working with large teams of robots is a very complex and demanding task for any operator and individual differences in spatial ability could significantly affect that performance. In the present study, we examine data from two earlier experiments to investigate the effects of ability for perspective-taking on performance at an urban search and rescue (USAR) task using a realistic simulation and alternate displays. We evaluated the participants' spatial ability using a standard measure of spatial orientation and examined the divergence of performance in accuracy and speed in locating victims, and perceived workload. Our findings show operators with higher spatial ability experienced less workload and marked victims more precisely. An interaction was found for the experimental image queue display for which participants with low spatial ability improved significantly in their accuracy in marking victims over the traditional streaming video display. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved

Characterization of a Multi-User Indoor Positioning System Based on Low Cost Depth Vision (Kinect) for Monitoring Human Activity in a Smart Home

An increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community