Collaborative augmented reality
Communications of the ACM - How the virtual inspires the real
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Placing Artificial Visual Landmarks in a Mobile Robot Workspace
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Optimal sensor placement for agent localization
ACM Transactions on Sensor Networks (TOSN)
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
Landmark Selection for Vision-Based Navigation
IEEE Transactions on Robotics
Landmark Selection for Task-Oriented Navigation
IEEE Transactions on Robotics
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Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulations and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.