Natural Landmark Detection for Visually-Guided Robot Navigation
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Region and constellations based categorization of images with unsupervised graph learning
Image and Vision Computing
The quantitative characterization of the distinctiveness and robustness of local image descriptors
Image and Vision Computing
Topological mapping with weak sensory data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Which landmark is useful?: learning selection policies for navigation in unknown environments
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
Pure topological mapping in mobile robotics
IEEE Transactions on Robotics
Effective landmark placement for accurate and reliable mobile robot navigation
Robotics and Autonomous Systems
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Recent work in the object recognition community has yielded a class of interest-point-based features that are stable under significant changes in scale, viewpoint, and illumination, making them ideally suited to landmark-based navigation. Although many such features may be visible in a given view of the robot's environment, only a few such features are necessary to estimate the robot's position and orientation. In this paper, we address the problem of automatically selecting, from the entire set of features visible in the robot's environment, the minimum (optimal) set by which the robot can navigate its environment. Specifically, we decompose the world into a small number of maximally sized regions, such that at each position in a given region, the same small set of features is visible. We introduce a novel graph theoretic formulation of the problem, and prove that it is NP-complete. Next, we introduce a number of approximation algorithms and evaluate them on both synthetic and real data. Finally, we use the decompositions from the real image data to measure the localization performance versus the undecomposed map