Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Analogy-making as perception: a computer model
Analogy-making as perception: a computer model
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
Evidential Reasoning for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biomimetic whiskers for shape recognition
Robotics and Autonomous Systems
Simultaneous Localization, Mapping and Moving Object Tracking
International Journal of Robotics Research
Probabilistic models with unknown objects
Probabilistic models with unknown objects
HERB: a home exploring robotic butler
Autonomous Robots
GATMO: a generalized approach to tracking movable objects
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
CrunchBot: a mobile whiskered robot platform
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Mapping with sparse local sensors and strong hierarchical priors
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Whisker-based texture discrimination on a mobile robot
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Robotics and Autonomous Systems
Where wall-following works: case study of simple heuristics vs. optimal exploratory behaviour
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
Efficient coding in the whisker system: biomimetic pre-processing for robots?
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
Biomimetic tactile target acquisition, tracking and capture
Robotics and Autonomous Systems
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The paradigm case for robotic mapping assumes large quantities of sensory information which allow the use of relatively weak priors. In contrast, the present study considers the mapping problem for a mobile robot, CrunchBot, where only sparse, local tactile information from whisker sensors is available. To compensate for such weak likelihood information, we make use of low-level signal processing and strong hierarchical object priors. Hierarchical models were popular in classical blackboard systems but are here applied in a Bayesian setting as a mapping algorithm. The hierarchical models require reports of whisker distance to contact and of surface orientation at contact, and we demonstrate that this information can be retrieved by classifiers from strain data collected by CrunchBot's physical whiskers. We then provide a demonstration in simulation of how this information can be used to build maps (but not yet full SLAM) in an zero-odometry-noise environment containing walls and table-like hierarchical objects.