Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors

Abstract

Wearable devices embedding inertial sensors enable autonomous, seamless, and low-cost pedestrian navigation. As appealing as it is, the approach faces several challenges: measurement noises, different device-carrying modes, different user dynamics, and individual walking characteristics. Recent research applies artificial intelligence (AI) to improve inertial navigation's robustness and accuracy. Our analysis identifies two main categories of AI approaches depending on the inertial signals segmentation: 1) either using human gait events (steps or strides) or 2) fixed-length inertial data segments. A theoretical analysis of the fundamental assumptions is carried out for each category. Two state-of-the-art AI algorithms (SELDA, RoNIN), representative of each category, and a gait-driven non-AI method (SmartWalk) are evaluated in a 2.17-km-long open-access dataset, representative of the diversity of pedestrians' mobility surroundings (open-sky, indoors, forest, urban, parking lot). SELDA is an AI-based stride length estimation algorithm, RoNIN is an AI-based positioning method, and SmartWalk is a gait-driven non-AI positioning method. The experimental assessment shows the distinct features in each category and their limits with respect to the underlying hypotheses. On average, SELDA, RoNIN, and SmartWalk achieve 8-m, 22-m, and 17-m average positioning errors (RMSE), respectively, on six testing tracks recorded with two volunteers in various environments.