TY - GEN
T1 - Using sound to classify vehicle-terrain interactions in outdoor environments
AU - Libby, Jacqueline
AU - Stentz, Anthony J.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84864429102&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864429102&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2012.6225357
DO - 10.1109/ICRA.2012.6225357
M3 - Conference contribution
AN - SCOPUS:84864429102
SN - 9781467314039
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3559
EP - 3566
BT - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
T2 - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
Y2 - 14 May 2012 through 18 May 2012
ER -