The flexible carrying mode of smartphone brings challenge for Pedestrian Dead Reckoning (PDR) especially for heading estimation with build-in sensors. This paper focuses on POCKET mode and SWING mode and analyzes the correlation between smartphone’s rotational motion and pedestrian’s walking cycle states and walking direction. Based on the analysis, we propose to use the rotation vector sensor data of smartphone within one walking step to estimate the pedestrian’s heading. For POCKET mode, heading is estimated by an improved rotational approach (IRA). A jitter detection algorithm is proposed to extract leg flexion interval. Stable walking direction without 180° ambiguity is obtained from the averaged rotation axis. For SWING mode, a single-point (SP) method is proposed. Heading is estimated from the direction of smartphone’s y-axis when it is closest to the horizontal plane. The algorithms are validated with data collected by HUAWEI mate 10 smartphone. The RMS errors are less than 4.37° and 3.38° for POCKET and SWING mode respectively. Superior to previous heading estimation algorithms, our method can converge within one single walking step for both carrying modes without 180° ambiguity.

Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity.