A survey of self-management in dynamic software architecture specifications
WOSS '04 Proceedings of the 1st ACM SIGSOFT workshop on Self-managed systems
Self-Managed Systems: an Architectural Challenge
FOSE '07 2007 Future of Software Engineering
Classifier fitness based on accuracy
Evolutionary Computation
EPnP: An Accurate O(n) Solution to the PnP Problem
International Journal of Computer Vision
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
Architecture-driven self-adaptation and self-management in robotics systems
SEAMS '09 Proceedings of the 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Self-adapting modular robotics: a generalized distributed consensus framework
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Double window optimisation for constant time visual SLAM
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Many mobile robot algorithms require tedious tuning of parameters and are, then, often suitable to only a limited number of situations. Yet, as mobile robots are to be employed in various fields from industrial settings to our private homes, changes in the environment will occur frequently. Organic computing principles such as self-organization, self-adaptation, or self-healing can provide solutions to react to new situations, e.g. provide fault tolerance. We therefore propose a biologically inspired self-adaptation scheme to enable complex algorithms to adapt to different environments. The proposed scheme is implemented using the Organic Robot Control Architecture (ORCA) and Learning Classifier Tables (LCT). Preliminary experiments are performed using a graph-based Visual Simultaneous Localization and Mapping (SLAM) algorithm and a publicly available benchmark set, showing improvements in terms of runtime and accuracy.