Abstract
Recently, the map matching-assisted positioning method based on micro-electromechanical systems (MEMS) inertial devices has become a research hotspot for indoor pedestrian positioning; however, these are based on existing indoor electronic maps. In this paper, without prior knowledge of the map and through building an indoor main path feature point map combined with the simultaneous localization and map building (SLAM) particle filter (PF-SLAM) algorithm idea, a PF-SLAM indoor pedestrian location algorithm based on a feature point map was proposed through the inertial measurement unit to improve indoor pedestrian positioning accuracy. Aiming at the problem of inaccurate heading angle estimation in the pedestrian dead reckoning (PDR) algorithm, a turn-straight-state threshold detection method was proposed that corrected the difference of the heading angles during the straight-line walking of pedestrians to suppress the error accumulation of the heading angle. Aiming at the particles that are severely divergent at the corners, a feature point matching algorithm was proposed to correct the pedestrian position error. Furthermore, the turning point extracted the main path that failed to match the current feature point map as a new feature point was added to update the map. Through the mutual modification of SLAM and an inertial navigation system (INS) the long-time, high-precision, and low-cost positioning functions of indoor pedestrians were realized.
Highlights
With the rapid development of modern digital information and the rise of smart cities, location based service (LBS) has attracted increasing attention [1]
Typical indoor pedestrian positioning technologies mainly include indoor positioning based on wireless networks [3,4], inertial navigation system (INS) based pedestrian dead reckoning (PDR) [5,6,7], positioning based on radio frequency (RF) signals [8,9], positioning technology based on geomagnetic matching [10], and various combinations of positioning technologies
In the absence of prior knowledge of indoor maps, this paper proposes a PF-simultaneous localization and map building (SLAM) indoor pedestrian location algorithm based on feature point maps by extracting feature points to construct the feature point map, and proposes a turn-straight-state threshold detection method to correct the heading angle difference and to suppress the heading error accumulation
Summary
With the rapid development of modern digital information and the rise of smart cities, location based service (LBS) has attracted increasing attention [1]. The simultaneous localization and map building (SLAM) algorithm originating from a robot does not need to prepare the environment map information in advance and can achieve the function of synchronous positioning and composition [15]. In the absence of prior knowledge of indoor maps, this paper proposes a PF-SLAM indoor pedestrian location algorithm based on feature point maps by extracting feature points to construct the feature point map, and proposes a turn-straight-state threshold detection method to correct the heading angle difference and to suppress the heading error accumulation. In combination with the SLAM idea, through the feature point matching algorithm, the problem of particle divergence at the turnings due to the inaccuracy of the heading angle error model was solved.
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