This paper presents a pedestrian navigation algorithm based on a foot-mounted 9-DOF Inertial Measurement Unit (IMU), which provides tri-axial accelerations, angular rates and magnetics. Most of 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.