Deep Learning for Inertial Positioning: A Survey

Inertial sensors are widely utilized in smartphones, drones, vehicles, and wearable devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors’ measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learning-based techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.

I. Introduction

The inertial Measurement Unit (IMU) is widely used in smartphones, drones, vehicles, and VR/AR devices. It continuously measures linear velocity and angular rate and tracks the motion of these platforms, as illustrated in Figure 1. With the advancements in Micro-Electro-Mechanical Systems (MEMS) technology, today’s MEMS IMUs are small, energy-efficient, and cost-effective. Inertial positioning (navigation) calculates attitude, velocity, and position based on inertial measurements, making it a crucial element in various location-based applications, including locating and navigating individuals in transportation infrastructures (e.g., airports, train stations) [1], supporting security and safety services (e.g., aiding first-responders) [2], enabling smart city/infrastructure, and facilitating human-device interaction [3]. Compared to other positioning solutions such as vision or radio, inertial positioning is completely ego-centric, works indoors and outdoors, and is less affected by environmental factors such as complex lighting conditions and scene dynamics. Fig. 1.

Inertial sensors are ubiquitous in modern platforms such as smartphones, drones, intelligent vehicles, and VR/AR devices. They play a critical role in enabling completely egocentric motion tracking and positioning, making them essential for a range of applications.