Miniaturized IMU (inertial measurement units) are widely integrated in wearable devices, promoting the versatile and low cost pedestrian inertial navigation technology, especially for indoor environment. In recent years, AI (Artificial Intelligence) is applied to improve the performance of this technology. AI methods work with data samples, thus it is important to select a suitable process for segmenting the inertial data sequences. This survey classifies AI methods for pedestrian inertial navigation into two categories, namely human gait driven methods and sampling frequency driven methods, according to their data segmentation process. Human gait driven methods segment the inertial measurement sequence by gait (step or stride) events and learn to infer a gait vector (step/stride length and direction) given a gait segment. Sampling frequency driven methods learn to infer the user’s velocity or change in position given a fixedlength segment of inertial measurements. The survey studies the underlying assumptions and their validity of the two categories of AI methods. Two methods (SELDA and RoNIN), each from a category, are chosen for evaluation and comparison, on three testing tracks totaling 770m, covering indoor and outdoor environment, including stairs. The experiments highlight the two methods’ advantages and limitations, supporting the theoretical analyses. The selected methods achieve 7m and 12m positioning errors, respectively.