Martin Persson, Tom Duckett, Christoffer Valgren and Achim J. Lilienthal
Probabilistic Semantic Mapping with a Virtual Sensor for Building/Nature Detection
Proc. IEEE Int. Symp. on Computational Intelligence in Robotics and Automation (CIRA), 2007, pp. 236-242
Abstract: In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We believe that access to semantic maps will make it possible for robots to better communicate information to a human operator and vice versa. The main contribution of this paper is a method that fuses data from different sensor modalities, range sensors and vision sensors are considered, to create a probabilistic semantic map of an outdoor environment. The method combines a learned virtual sensor (understood as one or several physical sensors with a dedicated signal processing unit for recognition of real world concepts) for building detection with a standard occupancy map. The virtual sensor is applied on a mobile robot, combining classifications of sub-images from a panoramic view with spatial information (location and orientation of the robot) giving the likely locations of buildings. This information is combined with an occupancy map to calculate a probabilistic semantic map. Our experiments with an outdoor mobile robot show that the method produces semantic maps with correct labeling and an evident distinction between "building" objects from "nature" objects.
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@INPROCEEDINGS{Persson_etal:CIRA:2007,
  AUTHOR = {Persson, Martin and Duckett, Tom and Valgren, Christoffer and Lilienthal, Achim J.},
  TITLE = {Probabilistic Semantic Mapping with a Virtual Sensor for Building/Nature Detection},
  BOOKTITLE = {Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)},
  YEAR = {2007},
  MONTH = {June 21--23},
  ADRESS = {Jacksonville, Florida, USA},
  PAGES = {236--242}
}