Abstract
In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system’s accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.
Highlights
Reliable indoor pedestrian navigation systems require devices and algorithms that can provide accurate, continuous, autonomous and stable position solutions in indoor environments
Through integrating indoor map information and built-in smart-phone inertial measurement unit (IMU), we present a self-dependent and easy to implement indoor-positioning method
When integrating the indoor map information with the INS system using the auxiliary particle filter (APF), whose solutions are shown in Figures 6b, 9b and 12b, the estimated position accuracy is dramatically increased, as the map information can strongly constrain the heading of the system by deleting the ineffective particles
Summary
Reliable indoor pedestrian navigation systems require devices and algorithms that can provide accurate, continuous, autonomous and stable position solutions in indoor environments. The indoor navigation system can effectively decrease the time and energy consumed to maneuver through large indoor environments, such as airports, hospitals, malls, and museums [1,2]. Precise indoor location results can help emergency workers perform urgent tasks by reducing the amount of time it takes to navigate these environments. Firefighters, and first responders could benefit from applications that provide navigation solutions for indoor search and rescue. In addition to how accurate positioning can satisfy a user’s basic needs, indoor navigation can be connected with customer relationship management (CRM) systems to explore the deep and added value of big data [3]. The market demand for indoor navigation systems is large and still growing, and more industrial companies, institutions, and universities are focusing their research efforts on indoor pedestrian navigation [4,5,6,7,8]
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