Measuring Vehicle Velocity in Real Time Using Modulated Motion Blur of Camera Image Data

In this paper, a novel sensor system is presented for estimating the velocity using a modulated motion blur. By moving a camera mounted on the vehicle body with a specific pattern when the vehicle is moving, the blurred image includes the information of the vehicle velocity of the camera itself. It will be shown that the inclinations of motion blur in a scene are directly related to the velocity vector of the vehicle and the modulation speed. The proposed approach invariant to the exposure time provides the magnitude and direction of the velocity vector with high accuracy and high reliability. In contrast to other approaches using a camera image, our approach requires only 256 × 192 [pixel], and the proposed algorithm is simple and fast. The efficacy of the proposed method is demonstrated through simulations and experiments. The experimental results present empirical evidence to support that the proposed system is robust to climate changes such as rainy or snowy weather. The proposed system is expected to be applicable to vehicular technologies such as the vehicle dynamics controlling system or the vehicle positioning system.

[1]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[2]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots , 2004 .

[3]  Huei Peng,et al.  A study on lateral speed estimation methods , 2004 .

[4]  Huei-Yung Lin,et al.  Motion blur removal and its application to vehicle speed detection , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[5]  Andrew Howard,et al.  Real-time stereo visual odometry for autonomous ground vehicles , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Frédo Durand,et al.  Motion-invariant photography , 2008, SIGGRAPH 2008.

[7]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[8]  Frédo Durand,et al.  Motion-invariant photography , 2008, ACM Trans. Graph..

[9]  Giacomo Boracchi Estimating the 3D direction of a translating camera from a single motion-blurred image , 2009, Pattern Recognit. Lett..

[10]  J. Mohammadi,et al.  Vehicle speed estimation based on the image motion blur using RADON transform , 2010, 2010 2nd International Conference on Signal Processing Systems.

[11]  T. S. Cho,et al.  Motion blur removal with orthogonal parabolic exposures , 2010, 2010 IEEE International Conference on Computational Photography (ICCP).

[12]  Nong Zhang,et al.  Stabilizing Vehicle Lateral Dynamics With Considerations of Parameter Uncertainties and Control Saturation Through Robust Yaw Control , 2010, IEEE Transactions on Vehicular Technology.

[13]  Andreas Geiger,et al.  Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[14]  Albert S. Huang,et al.  Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera , 2011, ISRR.

[15]  Ji Hoon Joung,et al.  What does ground tell us? Monocular visual odometry under planar motion constraint , 2011, 2011 11th International Conference on Control, Automation and Systems.

[16]  Domenico Grimaldi,et al.  Detection and parameters estimation of locally motion blurred objects , 2011, Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems.

[17]  Jian-Feng Cai,et al.  Framelet-Based Blind Motion Deblurring From a Single Image , 2012, IEEE Transactions on Image Processing.

[18]  Kyongsu Yi,et al.  Unified Chassis Control for the Improvement of Agility, Maneuverability, and Lateral Stability , 2012, IEEE Transactions on Vehicular Technology.

[19]  F. Fraundorfer,et al.  Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications , 2012, IEEE Robotics & Automation Magazine.

[20]  Myoungho Sunwoo,et al.  Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning , 2012, IEEE Transactions on Intelligent Transportation Systems.

[21]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Andreas Geiger,et al.  Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  André Platzer,et al.  On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles , 2013, Robotics: Science and Systems.

[24]  Fei Wang,et al.  Implementation of EKF for Vehicle Velocities Estimation on FPGA , 2013, IEEE Transactions on Industrial Electronics.

[25]  Shiyu Song,et al.  Robust Scale Estimation in Real-Time Monocular SFM for Autonomous Driving , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  José M. Bioucas-Dias,et al.  Parametric Blur Estimation for Blind Restoration of Natural Images: Linear Motion and Out-of-Focus , 2014, IEEE Transactions on Image Processing.

[27]  Motohiro Kawafuku,et al.  An application of state feedback control to actual vehicle vibration suppression , 2014, 2014 IEEE 13th International Workshop on Advanced Motion Control (AMC).

[28]  J. Rafid Siddiqui,et al.  Robust visual odometry estimation of road vehicle from dominant surfaces for large-scale mapping , 2015 .

[29]  Nabil Aouf,et al.  Multispectral Stereo Odometry , 2015, IEEE Transactions on Intelligent Transportation Systems.

[30]  Michael Felsberg,et al.  Robust stereo visual odometry from monocular techniques , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[31]  Masatoshi Ishikawa,et al.  Real-time high-speed motion blur compensation system based on back-and-forth motion control of galvanometer mirror. , 2015, Optics express.

[32]  Daniel D. Lee,et al.  Online self-supervised monocular visual odometry for ground vehicles , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[33]  Andreas Geiger,et al.  Map-Based Probabilistic Visual Self-Localization , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Clark C. Guest,et al.  High Accuracy Monocular SFM and Scale Correction for Autonomous Driving , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Paolo Valigi,et al.  Exploring Representation Learning With CNNs for Frame-to-Frame Ego-Motion Estimation , 2016, IEEE Robotics and Automation Letters.