Using Sound to Classify Vehicle-Terrain Interactions in Outdoor Environments

Jacqueline Libby and Anthony (Tony) Stentz
2012 IEEE International Conference on Robotics & Automation (ICRA 2012), May, 2012.


Download
  • Adobe portable document format (pdf) (1MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Robots that operate in complex physical environments can improve the accuracy of their perception systems by fusing data from complementary sensing modalities. Furthermore, robots capable of motion can physically interact with these environments, and then leverage the sensory information they receive from these interactions. This paper explores the use of sound data as a new type of sensing modality to classify vehicle-terrain interactions from mobile robots operating outdoors, which can complement more typical non-contact sensors that are used for terrain classification. Acoustic data from microphones was recorded on a mobile robot interacting with different types of terrains and objects in outdoor environments. This data was then labeled and used offline to train a supervised multiclass classifier that can distinguish between these interactions based on acoustic data alone. To the best of the author’s knowledge, this is the first time that acoustics has been used to classify a variety of interactions that a vehicle can have with its environment, so part of our contribution is to survey acoustic techniques from other domains and explore their efficacy for this application. The feature extraction methods we implement are derived from this survey, which then serve as inputs to our classifier. The multiclass classifier is then built from Support Vector Machines (SVMs). The results presented show an average of 92% accuracy across all classes, which suggest strong potential for acoustics to enhance perception systems on mobile robots.

Notes
Number of pages: 8

Text Reference
Jacqueline Libby and Anthony (Tony) Stentz, "Using Sound to Classify Vehicle-Terrain Interactions in Outdoor Environments ," 2012 IEEE International Conference on Robotics & Automation (ICRA 2012), May, 2012.

BibTeX Reference
@inproceedings{Libby_2012_7066,
   author = "Jacqueline Libby and Anthony (Tony) Stentz",
   title = "Using Sound to Classify Vehicle-Terrain Interactions in Outdoor Environments ",
   booktitle = "2012 IEEE International Conference on Robotics & Automation (ICRA 2012)",
   month = "May",
   year = "2012",
   number= "CMU-RI-TR-",
}