Hybrid Localization: A Low Cost, Low Complexity Approach Based on Wi-Fi and Odometry

Abstract

Localization in indoor environments is essential to further support automation in many scenarios such as warehouses and factories. Moreover, direction-of-arrival knowledge is essential to supporting high speed millimeter-wave (mmWave) links in indoor environments, since most mmWave links are of a line-of-sight nature to combat the high pathloss in this band. Accurate localization in indoor environments, however, has proved a challenging task due to multi- path fading methods such as trilateration alone do not result in accurate localization. As such, in this paper we propose to combine the knowledge of wireless localization methods with other sensors used for odometry to track the location of a mobile robot. This paper presents significant real world localization measurement results for both Wi-Fi and odometry in diverse environments at the Boise State University campus. Using these results, we devise an algorithm to combine data from both odometry and wireless localization. This algorithm is shown in hardware testing to enhance the localization accuracy by reducing the localization error for a mobile robot.