Neogeography: The Challenge of Channelling Large and Ill-Behaved Data Streams

Neogeography is the combination of user generated data and experiences with mapping technologies. In this article we present a research project to extract valuable structured information with a geographic component from unstructured user generated text in wikis, forums, or SMSes. The extracted information should be integrated together to form a collective knowledge about certain domain. This structured information can be used further to help users from the same domain who want to get information using simple question answering system. The project intends to help workers communities in developing countries to share their knowledge, providing a simple and cheap way to contribute and get benefit using the available communication technology

A Rao-Blackwellized Particle Filter for Topological Mapping

©2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2006 IEEE International Conference on Robotics and Automation (ICRA), 15-19 May 2006, Orlanda, FL.DOI: 10.1109/ROBOT.2006.1641809We present a particle filtering algorithm to construct topological maps of an uninstrument environment. The algorithm presented here constructs the posterior on the space of all possible topologies given measurements, and is based on our previous work on a Bayesian inference framework for topological maps [21]. Constructing the posterior solves the perceptual aliasing problem in a general, robust manner. The use of a Rao-Blackwellized Particle Filter (RBPF) for this purpose makes the inference in the space of topologies incremental and run in real-time. The RBPF maintains the joint posterior on topological maps and locations of landmarks. We demonstrate that, using the landmark locations thus obtained, the global metric map can be obtained from the topological map generated by our algorithm through a simple post-processing step. A data-driven proposal is provided to overcome the degeneracy problem inherent in particle filters. The use of a Dirichlet process prior on landmark labels is also a novel aspect of this work. We use laser range scan and odometry measurements to present experimental results on a robot

Simultaneous Localization and Map Building: A Global Topological Model with Local Metric Maps

In this paper an approach combining the metric and topological paradigm for simultaneous localization and map building is presented. The main idea is to connect local metric maps by means of a global topological map. This allows a compact environment model which does not require global metric consistency and permits both precision and robustness. The method uses a 360 degree laser scanner in order to extract corners and openings for the topological approach and lines for the metric localization. The approach has been tested in a 30 x 25 m portion of the institute building with the fully autonomous robot Donald Duck. An experiment consists of a complete exploration and a set of test missions. Three experiments have been performed for a total of 15 test missions, which have been randomly defined and completed with a success ratio of 87%

Combining Topological and Metric: A Natural Integration for Simultaneous Localization and Map Building

In this paper the metric and topological paradigm are integrated in a single system for both localization and map building. A global topological map connects local metric maps, allowing a compact environment model, which does not require global metric consistency and permits both precision and robustness. Furthermore, the approach permits to handle loops in the environment by automatic mapping using the information of the multimodal topological localization. The system uses a 360 degree laser scanner to extract corners and openings for the topological approach and lines for the metric method. This hybrid approach has been tested in a 50 x 25 m2 portion of the institute building with the fully autonomous robot Donald Duck. Experiments are of three types: Maps created by a complete exploration of the environment are compared to estimate their quality; Test missions are randomly generated in order to evaluate the efficiency of the localization approach; The third type of experiments shows the practicability of the approach for closing the loop

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

The Science of Networks: Urban Movement Design, Analytics, and Navigation

The science of networks, a relatively young field of research that appeared in such a form and definition at the beginning of the 21st century (as a distinctive, officially approved and accepted scientific discipline (Barabási, 2016)), represents a very powerful area considering the range of subjects to which it contributes and is applied. This science is key for complex systems analysis or analytics (when referring to the (big) data science framework, which now mostly defines its methods and resources (Betty, 2019)) based on the claim that networks encode the interactions between the system’s components (Barabási, 2016) and thus provide insights into the ways complex systems behave, or control the behaviour of the artificially created systems (emphasis added). The area herewith represented through its analytical methods and forms (network graphs and related operations) is the urban transportation system — the Grand Paris rail system, including all the categories with their existing lines and extensions currently either in construction and planned, or under consideration in the long term. The network has been created as a background topological environment and geometry for various research operations and generative design tasks. Some of them, such as urban movement path generation or the network’s incremental growth and reconfiguration as a system and the geometry of possible moves (legal actions), will be presented in more detail. The network can be considered both an abstract and real-world environment and situation, susceptible to the research of both gaming strategies for any constructed scenario and designed spatial situation (academic gaming, operational gaming, and heuristic gaming) and problem-solving strategies related to identified real-world design issues. Thus, the main question posed before the presented graph addresses the ways in which it can be operationalised and the methods through which this can be achieved, with special regard to AI.Invited Conference Contribution - Poster Section / Pape

Hybrid, metric - topological, mobile robot navigation

This thesis presents a recent research on the problem of environmental modeling for both localization and map building for wheel-based, differential driven, fully autonomous and self-contained mobile robots. The robots behave in an indoor office environment. They have a multi-sensor setup where the encoders are used for odometry and two exteroperceptive sensors, a 360° laser scanner and a monocular vision system, are employed to perceive the surrounding. The whole approach is feature based meaning that instead of directly using the raw data from the sensor features are firstly extracted. This allows the filtering of noise from the sensors and permits taking account of the dynamics in the environment. Furthermore, a properly chosen feature extraction has the characteristic of better isolating informative patterns. When describing these features care has to be taken that the uncertainty from the measurements is taken into account. The representation of the environment is crucial for mobile robot navigation. The model defines which perception capabilities are required and also which navigation technique is allowed to be used. The presented environmental model is both metric and topological. By coherently combining the two paradigms the advantages of both methods are added in order to face the drawbacks of a single approach. The capabilities of the hybrid approach are exploited to model an indoor office environment where metric information is used locally in structures (rooms, offices), which are naturally defined by the environment itself while the topology of the whole environment is resumed separately thus avoiding the need of global metric consistency. The hybrid model permits the use of two different and complementary approaches for localization, map building and planning. This combination permits the grouping of all the characteristics which enables the following goals to be met: Precision, robustness and practicability. Metric approaches are, per definition, precise. The use of an Extended Kalman Filter (EKF) permits to have a precision which is just bounded by the quality of the sensor data. Topological approaches can easily handle large environments because they do not heavily rely on dead reckoning. Global consistency can, therefore, be maintained for large environments. Consistent mapping, which handle large environments, is achieved by choosing a topological localization approach, based on a Partially Observable Markov Decision Process (POMDP), which is extended to simultaneous localization and map building. The theory can be mathematically proven by making some assumptions. However, as stated during the whole work, at the end the robot itself has to show how good the theory is when used in the real world. For this extensive experimentation for a total of more than 9 km is performed with fully autonomous self-contained robots. These experiments are then carefully analyzed. With the metric approach precision with error bounds of about 1 cm and less than 1 degree is further confirmed by ground truth measurements with a mean error of less than 1 cm. The topological approach is successfully tested by simultaneous localization and map building where the automatically created maps turned out to work better than the a priori maps. Relocation and closing the loop are also successfully tested