Reliability Estimation of Vehicle Localization Result

This paper proposes a method for estimation of the reliability of vehicle localization results. We previously proposed a fault detection method for indoor mobile robots using a convolutional neural network (CNN). Because image data is generally fed to a CNN, we feed image data obtained from the robot pose, occupancy grid map, and laser scan data to the CNN, which decides of whether localization has failed. The previous method also employed a Rao-Blackwellized particle filter to estimate the robot pose and reliability of this estimation simultaneously. However, it was difficult for vehicle robots to use the previous method as creating and processing image data is not a light computation process. In this study, we extend the previous method by improving the data fed to the CNN, thus making it possible for vehicle robots to perform simultaneous localization and estimation. This paper describes in detail the simultaneous estimation and shows that the reliability can be used as an exact criterion for detecting localization failures. Keywords-Vehicle Localization, Reliability

[1]  Pedro Albertos,et al.  Multi Sensor Fusion Framework for Indoor-Outdoor Localization of Limited Resource Mobile Robots , 2013, Sensors.

[2]  Takashi Tsubouchi,et al.  A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Qianfan Xin,et al.  Diesel Engine System Design , 2011 .

[4]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Albert-Jan Baerveldt,et al.  Localization in changing environments - estimation of a covariance matrix for the IDC algorithm , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[6]  S. Thrun,et al.  Particle Filters for Rover Fault Diagnosis , 2004 .

[7]  Andrea Censi,et al.  An accurate closed-form estimate of ICP's covariance , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[8]  L. Hsu.  GNSS Mulitpath Detection Using a Machine Learning Approach , 2017 .

[9]  Edwin Olson,et al.  Real-time correlative scan matching , 2009, 2009 IEEE International Conference on Robotics and Automation.

[10]  Eijiro Takeuchi,et al.  Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[11]  Gaurav S. Sukhatme,et al.  Fault detection and identification in a mobile robot using multiple model estimation and neural network , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[12]  Fawzi Nashashibi,et al.  Failure detection for laser-based SLAM in urban and peri-urban environments , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[13]  Dieter Fox,et al.  An experimental comparison of localization methods continued , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Oliver Urbann,et al.  Efficient Multi-hypotheses Unscented Kalman Filtering for Robust Localization , 2011, RoboCup.

[15]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[16]  Nikos Komodakis,et al.  Learning to compare image patches via convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Eijiro Takeuchi,et al.  Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[18]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[19]  Hiroshi Murase,et al.  Simultaneous pose and reliability estimation using convolutional neural network and Rao–Blackwellized particle filter , 2018, Adv. Robotics.

[20]  Patric Jensfelt,et al.  Fault detection for mobile robots using redundant positioning systems , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[21]  Manuela Veloso,et al.  Mobile Robot Fault Detection based on Redundant Information Statistics , 2012 .

[22]  Marios M. Polycarpou,et al.  Fault detection for service mobile robots using model-based method , 2015, Autonomous Robots.

[23]  Yoshitaka Hara,et al.  6DOF iterative closest point matching considering a priori with maximum a posteriori estimation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.