Exploring artificial intelligence in the new millennium
A Set Theoretic Approach to Dynamic Robot Localization and Mapping
Autonomous Robots
Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data
Journal of Intelligent and Robotic Systems
Global Path Planning in Gaussian Probabilistic Maps
Journal of Intelligent and Robotic Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robocentric map joining: Improving the consistency of EKF-SLAM
Robotics and Autonomous Systems
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using dynamic time warping for online temporal fusion in multisensor systems
Information Fusion
Modeling dynamic scenarios for local sensor-based motion planning
Autonomous Robots
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Drift-Free Real-Time Sequential Mosaicing
International Journal of Computer Vision
The use of a Reasoning process to solve the almost SLAM Challenge at the Robocup legged league
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Foundations and Trends in Robotics
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A nonlinear set membership approach for the localization and map building of underwater robots
IEEE Transactions on Robotics
Simultaneous localization and mapping: A feature-based probabilistic approach
International Journal of Applied Mathematics and Computer Science - Special Section: Robot Control Theory Cezary Zielinski
On-line visual vocabularies for robot navigation and mapping
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Conjugate mixture models for clustering multimodal data
Neural Computation
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Survey of Motion Planning Literature in the Presence of Uncertainty: Considerations for UAV Guidance
Journal of Intelligent and Robotic Systems
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Cooperative SLAM using M-Space representation of linear features
Robotics and Autonomous Systems
Cascaded Evolutionary Estimator for Robot Localization
International Journal of Applied Evolutionary Computation
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From the Publisher:During the last decade, many researchers have dedicated their efforts to constructing revolutionary machines and to providing them with forms of artificial intelligence to perform some of the most hazardous, risky or monotonous tasks historically assigned to human beings. Among those machines, mobile robots are undoubtedly at the cutting edge of current research directions. A rough classification of mobile robots can be considered: on the one hand, mobile robots oriented to human-made indoor environments; on the other hand, mobile robots oriented to unstructured outdoor environments, which could include flying oriented robots, space-oriented robots and underwater robots. The most common motion mechanism for surface mobile robots is the wheel-based mechanism, adapted both to flat surfaces, found in human-made environments, and to rough terrain, found in outdoor environments. However, some researchers have reported successful developments with leg-based mobile robots capable of climbing up stairs, although they require further investigation. The research work presented here focuses on wheel-based mobile robots that navigate in human-made indoor environments. The main problems described throughout this book are: Representation and integration of uncertain geometric information by means of the Symmetries and Perturbations Model (SPmodel). This model combines the use of probability theory to represent the imprecision in the location of a geometric element, and the theory of symmetries to represent the partiality due to characteristics of each type of geometric element. A solution to the first location problem, that is, the computation of an estimation for themobile robot location when the vehicle is completely lost in the environment. The problem is formulated as a search in an interpretation tree using efficient matching algorithms and geometric constraints to reduce the size of the solution space. The book proposes a new probabilistic framework adapted to the problem of simultaneous localization and map building for mobile robots: the Symmetries and Perturbations Map (SPmap). This framework has been experimentally validated by a complete experiment which profited from ground-truth to accurately validate the precision and the appropriateness of the approach. The book emphasizes the generality of the solutions proposed to the different problems and their independence with respect to the exteroceptive sensors mounted on the mobile robot. Theoretical results are complemented by real experiments, where the use of multisensor-based approaches is highlighted.