Commenced in January 2007
Paper Count: 30848
Terrain Classification for Ground Robots Based on Acoustic Features
Abstract:The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130637Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 712
 Debangshu Sadhukhan, Carl Moore, and Emmanuel Collins, “Terrain estimation using internal sensors,” in Proceedings of the 10th IASTED International Conference on Robotics and Applications (RA), 2004.
 David Tick, Tauhidur Rahman, Carlos Busso, and Nicholas Gans, “Indoor robotic terrain classification via angular velocity based hierarchical classifier selection,” in Robotics and Automation (ICRA), 2012 IEEE International Conference on. IEEE, 2012, pp. 3594–3600.
 Eric Coyle, Emmanuel G Collins Jr, and Rodney G Roberts, “Speed independent terrain classification using singular value decomposition interpolation,” in Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, 2011, pp. 4014–4019.
 Ayanna Howard and Homayoun Seraji, “Vision-based terrain characterization and traversability assessment,” Journal of Robotic Systems, vol. 18, no. 10, pp. 577–587, 2001.
 Jann Poppinga, Andreas Birk, and Kaustubh Pathak, “Hough based terrain classification for realtime detection of drivable ground,” Journal of Field Robotics, vol. 25, no. 1, pp. 67, 2008.
 Liang Lu, Camilo Ordonez, Emmanuel G Collins Jr, and Edmond M DuPont, “Terrain surface classification for autonomous ground vehicles using a 2d laser stripe-based structured light sensor,” in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on. IEEE, 2009, pp. 2174–2181.
 Christopher Brooks, Karl Iagnemma, et al., “Vibration-based terrain classification for planetary exploration rovers,” Robotics, IEEE Transactions on, vol. 21, no. 6, pp. 1185–1191, 2005.
 Christian Weiss, Holger Fr¨ohlich, and Andreas Zell, “Vibration-based terrain classification using support vector machines,” in Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on. IEEE, 2006, pp. 4429–4434.
 Chris C Ward and Karl Iagnemma, “Speed-independent vibration-based terrain classification for passenger vehicles,” Vehicle System Dynamics, vol. 47, no. 9, pp. 1095–1113, 2009.
 Jacqueline Libby and Anthony J. Stentz, “Using sound to classify vehicle-terrain interactions in outdoor environments,” in Robotics and Automation (ICRA), 2012 IEEE International Conference on. IEEE, 2012, pp. 3559–3566.
 Karl D Iagnemma and Steven Dubowsky, “Terrain estimation for high-speed rough-terrain autonomous vehicle navigation,” in AeroSense 2002. International Society for Optics and Photonics, 2002, pp. 256–266.
 Karl Iagnemma, Hassan Shibly, and Steven Dubowsky, “On-line terrain parameter estimation for planetary rovers,” in Robotics and Automation, 2002. Proceedings. ICRA’02. IEEE International Conference on. IEEE, 2002, vol. 3, pp. 3142–3147.
 Ivana Kruijff-Korbayov´a, Francis Colas, Mario Gianni, Fiora Pirri, Joachim de Greeff, Koen Hindriks, Mark Neerincx, Petter O¨ gren, Toma´sˇ Svoboda, and Rainer Worst, “TRADR project: Long-term human-robot teaming for robot assisted disaster response,” KI-K¨unstliche Intelligenz, pp. 1–9, 2015.
 Theodoros Giannakopoulos, Dimitrios Kosmopoulos, Andreas Aristidou, and Sergios Theodoridis, “Violence content classification using audio features,” in Advances in Artificial Intelligence, pp. 502–507. Springer, 2006.
 Mark C Wellman, Nino Srour, and David B Hillis, “Feature extraction and fusion of acoustic and seismic sensors for target identification,” in AeroSense’97. International Society for Optics and Photonics, 1997, pp. 139–145.
 Abraham Gebru Tesfay, “Audio data collection for terrain classification from sound,” Available for Download under http://ox6.dfki.de/publications/infostore/10/Bernd%20Kiefer?secret= 694424e304f6dbb14f3b1eaff75b9e83.
 Michal Reinstein, Vladimir Kubelka, and Karsten Zimmermann, “Terrain adaptive odometry for mobile skid-steer robots,” in Robotics and automation (icra), 2013 ieee international conference on. IEEE, 2013, pp. 4706–4711.
 Absolem surveillance and rescue, Available: http://www.bluebotics.com/ mobile-robotics/absolem/ (Accessed 27 March 2017).