Anytime online novelty detection for vehicle safeguarding

Novelty detection is often treated as a one-class classification problem: how to segment a data set of examples from everything else that would be considered novel or abnormal. Almost all existing novelty detection techniques, however, suffer from diminished performance when the number of less relevant, redundant or noisy features increases, as often the case with high-dimensional feature spaces. Many of these algorithms are also not suited for online use, a trait that is highly desirable for many robotic applications. We present a novelty detection algorithm that is able to address this sensitivity to high feature dimensionality by utilizing prior class information within the training set. Additionally, our anytime algorithm is well suited for online use when a constantly adjusting environmental model is beneficial. We apply this algorithm to online detection of novel perception system input on an outdoor mobile robot and argue such abilities could be key in increasing the real-world applications and impact of mobile robotics1.

[1]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[2]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[3]  Alexander J. Smola,et al.  Online learning with kernels , 2001, IEEE Transactions on Signal Processing.

[4]  Bernhard Schölkopf,et al.  Support Vector Method for Novelty Detection , 1999, NIPS.

[5]  Stephen R. Marsland,et al.  On-line novelty detection for autonomous mobile robots , 2005, Robotics Auton. Syst..

[6]  David Silver,et al.  Learning to search: Functional gradient techniques for imitation learning , 2009, Auton. Robots.

[7]  Heiko Hoffmann,et al.  Kernel PCA for novelty detection , 2007, Pattern Recognit..

[8]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[9]  David M. Bradley,et al.  Vegetation Detection for Driving in Complex Environments , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[10]  고한석,et al.  컬러 영상에서 Support Vector Domain Description을 이용한 얼굴 검출 , 2005 .

[11]  Martial Hebert,et al.  Natural terrain classification using three‐dimensional ladar data for ground robot mobility , 2006, J. Field Robotics.

[12]  Hugo Vieira Neto,et al.  Visual novelty detection with automatic scale selection , 2007, Robotics Auton. Syst..

[13]  Nathalie Japkowicz,et al.  A Novelty Detection Approach to Classification , 1995, IJCAI.

[14]  Colin Campbell,et al.  A Linear Programming Approach to Novelty Detection , 2000, NIPS.

[15]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[16]  Anthony Stentz,et al.  The Crusher System for Autonomous Navigation , 2007 .

[17]  Keith Worden,et al.  STRUCTURAL FAULT DETECTION USING A NOVELTY MEASURE , 1997 .

[18]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[19]  Robert E. Tarjan,et al.  Amortized efficiency of list update and paging rules , 1985, CACM.

[20]  Risto Miikkulainen,et al.  Intrusion Detection with Neural Networks , 1997, NIPS.

[21]  Bernhard Schölkopf,et al.  Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra , 2000, NIPS.

[22]  Alonzo Kelly,et al.  Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments , 2006, Int. J. Robotics Res..

[23]  Michael Brady,et al.  Novelty detection for the identification of masses in mammograms , 1995 .

[24]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[25]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[26]  David G. Stork,et al.  Pattern Classification , 1973 .

[27]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.