Surface Type Classification for Autonomous Robot Indoor Navigation

In this work we describe the preparation of a time series dataset of inertial measurements for determining the surface type under a wheeled robot. The data consists of over 7600 labeled time series samples, with the corresponding surface type annotation. This data was used in two public competitions with over 1500 participant in total. Additionally, we describe the performance of state-of-art deep learning models for time series classification, as well as propose a baseline model based on an ensemble of machine learning methods. The baseline achieves an accuracy of over 68% with our nine-category dataset.

[1]  Heikki Huttunen,et al.  ACOUSTIC SCENE CLASSIFICATION: A COMPETITION REVIEW , 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

[2]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[3]  Tim Oates,et al.  Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[4]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[5]  J. Kuipers.  Quaternions and Rotation Sequences , 1998 .

[6]  K. Walas.  Terrain Classification and Negotiation with a Walking Robot , 2015, J. Intell. Robotic Syst..

[7]  Wolfram Burgard,et al.  Deep spatiotemporal models for robust proprioceptive terrain classification , 2017, Int. J. Robotics Res..

[8]  Germain Forestier,et al.  Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.

[9]  T. Virtanen,et al.  Probabilistic Model Based Similarity Measures for Audio Query-by-Example , 2007, 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[10]  Dani Korpi,et al.  Method for creating location-specific audio textures , 2014, EURASIP J. Audio Speech Music. Process..

[11]  J. L. Maryak,et al.  A Feasible Bayesian Estimator of Quantiles for Projectile Accuracy From Non-iid Data , 1992 .

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[14]  Karl Iagnemma,et al.  Self‐supervised terrain classification for planetary surface exploration rovers , 2012, J. Field Robotics.

[15]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[16]  Mario F. M. Campos,et al.  Speed-invariant terrain roughness classification and control based on inertial sensors , 2017, 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR).

[17]  Csaba Kertész.  Rigidity-Based Surface Recognition for a Domestic Legged Robot , 2016, IEEE Robotics and Automation Letters.

[18]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[19]  Gary Witus,et al.  Terrain characterization and classification with a mobile robot , 2006, J. Field Robotics.