Indoor Pedestrian Localization With a Smartphone: A Comparison of Inertial and Vision-Based Methods

Abstract

Indoor pedestrian navigation systems are increasingly needed in various types of applications. However, such systems are still face many challenges. In addition to being accurate, a pedestrian positioning system must be mobile, cheap, and lightweight. Many approaches have been explored. In this paper, we take the advantage of sensors integrated in a smartphone and their capabilities to develop and compare two low-cost, hands-free, and handheld indoor navigation systems. The first one relies on embedded vision (smartphone camera), while the second option is based on low-cost smartphone inertial sensors (magnetometer, accelerometer, and gyroscope) to provide a relative position of the pedestrian. The two associated algorithms are computationally lightweight, since their implementations take into account the restricted resources of the smartphone. In the experiment conducted, we evaluate and compare the accuracy and repeatability of the two positioning methods for different indoor paths. The results obtained demonstrate that the visionbased localization system outperforms the inertial sensor-based positioning system.

3-D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

“…the Global Positioning System (GPS) for indoor use, there have been a significant amount of researches to design and develop an alternative indoor positioning technology. They have resulted in several solutions, which can be divided into two main categories: infrastructure-based, and infrastructure-free [1]. Infrastructure based methods require costly and labor intensive pre-installations or regular management of related infrastructures.…”

“…They include the foot-mounted [15][16][17][18][19][20], waistmounted [21] and hand-held systems [22][23][24][25][26]. This study proposes and implements a novel PDR system for handheld smartphones, to make most of their wide use and ubiquity [27,28], and also the miniaturized and low-cost sensors that are embedded in the phone [1]. However, the accumulating temporal drift is still the major challenge for many applications [20,29,30].…”

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors

“…Unlike outdoor localization where GNSS (Global Navigation Satellite System) have imposed themselves, there is no dominant technology. Ultrasound (US) [8], cameras [9], radio frequency (RF) [10,11], which also comprises UWB (Ultra Wide Band) technologies [12,13], and infrared (IR) have been mainly used so far. Alternatives with IR have the disadvantage of being directional, but they are very interesting when high precision is required in a channel without interference.…”

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

“…To address this problem, many machine learning based localization methods [5-8] have been developed, which learn a pattern of the RSSI measurements corresponding locations across the interested positioning area. In addition, due to its unbiased estimation capability, it is likely to be combined with other kinds of localization, such as pedestrian localization using inertial measurement unit (IMU) [9,10], visual localization [11,12], and magnetic sensor-based localization [13,14].In particular, semisupervised learning algorithms have been recently suggested for efficient indoor localization, which reduce the human effort necessary for collecting training data [15][16][17][18][19][20]. For example, for indoor localization, a large amount of unlabeled data can be easily collected by recording only Wi-Fi RSSI measurements without requiring position labels, which can save resources for collection and calibration.…”

“…To address this problem, many machine learning based localization methods [5-8] have been developed, which learn a pattern of the RSSI measurements corresponding locations across the interested positioning area. In addition, due to its unbiased estimation capability, it is likely to be combined with other kinds of localization, such as pedestrian localization using inertial measurement unit (IMU) [9,10], visual localization [11,12], and magnetic sensor-based localization [13,14].…”