Free-Walking 3D Pedestrian Large Trajectory Reconstruction from IMU Sensors

This paper presents a pedestrian navigation algorithm based on a foot-mounted 9 Degree of Freedom (DOF) Inertial Measurement Unit (IMU), which provides tri-axial accelerations, angular rates and magnetics. Most algorithms used worldwide employ Zero Velocity Update (ZUPT) to reduce the tremendous error of integration from acceleration to displacement. The crucial part in ZUPT is to detect stance phase precisely. A cyclic left-to-right style Hidden Markov Model is introduced in this work which is able to appropriately model the periodic nature of signals. Stance detection is then made unsupervised by using a suited learning algorithm. Then orientation estimation is performed independently by a quaternion-based method, a simplified error-state Extended Kalman Filter (EKF) assists trajectory reconstruction in 3D space, neither extra method nor prior knowledge is needed to estimate the height. Experimental results on large free-walking trajectories show that the proposed algorithm can provide more accurate locations, especially in z-axis compared to competitive algorithms, w.r.t. to a ground-truth obtained using OpenStreetMap.

[1]  Changdon Kee,et al.  Motion Recognition-Based 3D Pedestrian Navigation System Using Smartphone , 2016, IEEE Sensors Journal.

[2]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[3]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

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

[5]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[6]  Seung-Hwan Choi,et al.  Performance improvement and height estimation of pedestrian dead-reckoning system using a low cost MEMS sensor , 2012, 2012 12th International Conference on Control, Automation and Systems.

[7]  Angelo M. Sabatini,et al.  A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[9]  Huosheng Hu,et al.  Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion , 2018, Inf. Fusion.

[10]  Cheng Chen,et al.  2D/3D indoor navigation based on multi-sensor assisted pedestrian navigation in Wi-Fi environments , 2012, 2012 Ubiquitous Positioning, Indoor Navigation, and Location Based Service (UPINLBS).

[11]  Li Wang,et al.  Quaternion-Based Kalman Filter for AHRS Using an Adaptive-Step Gradient Descent Algorithm , 2015 .

[12]  Abdelmoumen Norrdine,et al.  Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet , 2016, IEEE Sensors Journal.

[13]  Wang Pu,et al.  Height error correction based on shoe-mounted pedestrian navigation systems , 2016, 2016 35th Chinese Control Conference (CCC).

[14]  Young Soo Suh,et al.  Height Compensation Using Ground Inclination Estimation in Inertial Sensor-Based Pedestrian Navigation , 2011, Sensors.

[15]  FoxlinEric.  Pedestrian Tracking with Shoe-Mounted Inertial Sensors , 2005 .