Smartphone-based pedestrian dead-reckoning (PDR) has become promising in indoor localization since it locates users with a smartphone only. However, existing PDR approaches are still facing the problem of accumulated localization errors due to low-cost noisy sensors and complicated human movements. This paper presents a novel PDR indoor localization algorithm combined with online sequential extreme learning machine (OS-ELM). By analyzing the process of PDR localization, this paper first formulates the process of PDR localization as an approximation function, and then, a sliding-window-based scheme is designed to preprocess the obtained inertial sensor data and thus to generate the feature dataset. At last, the OS-ELM-based PDR algorithm is proposed to address the localization problem of pedestrians. Due to the fact of universal approximation capability and extreme learning speed within OS-ELM, our algorithm can adapt to localization environment dynamically and reduce the localization errors to a low scale. In addition, by taking the movement habits of pedestrian into the process of extreme learning, our algorithm can predict the position of pedestrian regardless of holding postures. To evaluate the performance of the proposed algorithm, this paper implements OS-ELM-based PDR on a real android-based smartphone and compares it with the state-of-the-art approaches. Extensive experiment results demonstrate the effectiveness of the proposed algorithm in various different postures and the practicability in indoor localization.