Indoor trajectory identification: Snapping with uncertainty

Abstract

Abstract-We consider the problem of indoor human trajectory identification using odometry data from smartphone sensors. Given a segmented trajectory, a simplified map of the environment, and a set of error thresholds, we implement a map-matching algorithm in a urban setting and analyze the accuracy of the resulting path. We also discuss aggregation of user step data into a segmented trajectory. Besides providing an interesting application of learning human motion in a constrained environment, we examine how the uncertainty of the snapped trajectory varies with path length. We demonstrate that as new segments are added to a path, the number of possibilities for earlier segments is monotonically non-increasing. Applications of this work in an urban setting are discussed, as well as future plans to develop a formal theory of odometrybased map-matching. I. INTRODUCTIONIn this paper we examine the identification of walking trajectories of people equipped with mobile phone-based odometry sensors. Specifically, we build on prior work in [1], [2] and implement a "snapping" algorithm to reconstruct human paths traversed in a real indoor environment, given an existing map of that environment. Our algorithm searches for all plausible paths within specified error bounds using the map and a segmented trajectory derived from accelerometer and gyroscope measurements.There are three major modalities for indoor human path identification; WiFi, odometry, and vision-based. Visionbased systems rely on fixed infrastructure (cameras), odometry-based systems rely on mobile sensors, and WiFibased systems require both fixed WiFi access points in conjunction with a WiFi-enabled mobile device [3], [4]. A significant advantage of odometry over vision or WiFi-based systems is that there is no requirement for the installation of fixed hardware (this is also an advantage over WiFi based approaches), making scaling more cost-efficient. We emphasize that the odometry-based approach does not use GPS, and in fact uses no data apart from smartphone sensor measurements and an underlying "topological map" of the space.Our technique of map matching is borrowed from the navigation algorithms used for outdoor GPS[5], [6]. It was first used to handle indoor path identification tasks with a wheeled robot [1], [2], and proved robust in several real settings. Previous efforts relying only on cell phone odometry used probabilistic techniques like particle filters [7], which have strong independence assumptions between measurements. In contrast, our "snapping" approach focuses on using

A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML

“…Gua et al [62] improved the accuracy of the map matching process in indoor trajectories considering semantics provided by pedestrian dead reckoning and human activity recognition algorithms. Wang et al [63] used raw odometer data to extract step length, step count, and heading for the purpose of trajectory segmentation. In our research, the raw indoor trajectory is segmented by the definition of stay points and semantically enriched by OGC IndoorGML cell attributes.…”