Optical flow based step length estimation for indoor pedestrian navigation on a smartphone

In this paper, an optical flow based step length estimation algorithm for indoor pedestrian navigation is proposed. To address the challenge of interferences arising from hands shaking during walking, the pose of a smartphone is computed by attitude and heading reference system (AHRS) algorithm and used to improve the performance of optical flow algorithm. Moreover, the motion information of pedestrians can be captured by calculating the alteration and relevance between sequential pixels and frames of camera snapshots when steps are detected. Accordingly, online training and calibration of step length estimation in pedestrian dead-reckoning system (PDR) are accomplished. To verify the performance of proposed step length estimation algorithm, several field tests with a smartphone were conducted in various environments. Experimental results show that the proposed algorithm achieves accurate performance in terms of step length estimation.

[1]  Yan Cui,et al.  Pedestrian Navigation with INS Measurements and Gait Models , 2011 .

[2]  Helena Leppäkoski,et al.  Error Analysis of Step Length Estimation in Pedestrian Dead Reckoning , 2002 .

[3]  Yuwei Chen,et al.  A Smart Phone Based PDR Solution for Indoor Navigation , 2011 .

[4]  Yuwei Chen,et al.  Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning , 2012, Sensors.

[5]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[6]  Martin Klepal,et al.  Mobile Phone-Based Displacement Estimation for Opportunistic Localisation Systems , 2009, 2009 Third International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies.

[7]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[8]  Quentin Ladetto,et al.  On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering , 2000 .

[9]  Dong-Hwan Hwang,et al.  Robust step detection method for pedestrian navigation systems , 2007 .

[10]  Robert B. McGhee,et al.  An extended Kalman filter for quaternion-based orientation estimation using MARG sensors , 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).

[11]  Ling Pei,et al.  Motion Recognition Assisted Indoor Wireless Navigation on a Mobile Phone , 2010 .

[12]  Ruizhi Chen,et al.  A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS , 2012, Sensors.

[13]  David H. Warren,et al.  Electronic spatial sensing for the blind : contributions from perception, rehabilitation, and computer vision , 1985 .

[14]  Ching-Kun Chen,et al.  Step-length Estimation Using Wrist-worn Accelerometer and GPS , 2011 .

[15]  J.W. Kim,et al.  Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors , 2007, 2007 IEEE Sensors Applications Symposium.

[16]  H. Haas,et al.  Pedestrian Dead Reckoning : A Basis for Personal Positioning , 2006 .

[17]  Vincent Gabaglio.  Centralised Kalman Filter for Augmented GPS Pedestrian Navigation , 2001 .

[18]  Ellen M. Markman,et al.  Thinking in perspective: Critical essays in the study of thought processes. , 1979 .

[19]  R. Bharat Rao,et al.  Evolution of mobile location-based services , 2003, CACM.

[20]  Yuwei Chen,et al.  Using Motion-Awareness for the 3D Indoor Personal Navigation on a Smartphone , 2011 .

[21]  Peilin Liu,et al.  RPNOS: Reliable Pedestrian Navigation on a Smartphone , 2013, GRMSE.

[22]  Peilin Liu,et al.  An improved indoor localization method using smartphone inertial sensors , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[23]  Piotr Ptasinski,et al.  A method for dead reckoning parameter correction in pedestrian navigation system , 2003, IEEE Trans. Instrum. Meas..

[24]  Eric Foxlin,et al.  Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter , 1996, Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium.