Set-labelled filters and sensor transformations

Fatemeh Zahra Saberifar, Shervin Ghasemlou, Jason M. O'Kane, Dylan Shell
In Proc. Robotics: Science and Systems
2016

Abstract This paper addresses questions, for a given robot and a given task, about which modifications may be made to the robot's suite of sensors without impacting the robot's behavior in completing that task. Though this is an important design-time question, few principled methods exist for providing a definitive answer in general. Utilizing and extending the language of combinatorial filters, this paper aims to fill that lacuna by introducing theoretical tools for reasoning about sensors, representations of sensors. It introduces new representations for sensors and filters, exploring the relationship between those elements and the specific information needed to perform a task. It then shows how these tools can be used to algorithmically answer questions about changes to a robot's sensor suite. The paper substantially expands the expressiveness of combinatorial filters so that, where they were previously limited to quite simple sensors, our richer filters are able to reasonably model a much broader variety of real devices. We have implemented the proposed algorithms, and describe their application to an example instance involving a series of simplifications to the sensors of a specific, widely deployed mobile robot.

@inproceedings{SabGha+16,
  author = {Fatemeh Zahra       Saberifar and Shervin   Ghasemlou and Jason M.
            O'Kane and Dylan Shell},
  booktitle = {Proc. Robotics: Science and Systems},
  title = {Set-labelled filters and sensor transformations},
  year = {2016}
}


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