Abstract
Maps, and specifically floor plans, are useful for planning a variety of tasks from arranging furniture to designating conceptual or functional spaces (e.g., kitchen, walkway). However, maps generated directly from robot sensor data can be hard to interpret and use for this purpose, especially for individuals who are not used to them, because of sensor and odometry measurement errors and the probabilistic nature of the mapping algorithms themselves. In this paper, we present an algorithm for quickly laying a floor plan (or other conceptual map) onto a map generated from sensor data, creating a one-to-one mapping between the two This allows humans interacting with the robot to use a more readily-understandable representation of the world, while the robot itself uses the sensor-generated map.
We look at two use cases: specifying “no-go” regions within a room, and visually locating objects within a room. Although a user study showed no statistical difference between the two types of maps in terms of performance on this spatial memory task, we argue that floor plans are closer to the mental maps people naturally draw to characterize spaces, and are easier to use for untrained individuals.
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Acknowledgments
Research funded in part by NSF grants CNS 1359480 (NSF Site Robots in the Real World) and IIS 1340897 (RET supplement, HCC: Small: A Physical Vocabulary for Human-Robot Interaction).
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Bowen-Biggs, L. et al. (2016). A Method for Establishing Correspondences Between Hand-Drawn and Sensor-Generated Maps. In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_98
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DOI: https://doi.org/10.1007/978-3-319-47437-3_98
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