Some map matching algorithms for personal navigation assistants

White, Christopher E.; Bernstein, David; Kornhauser, Alain L.

doi:10.1016/s0968-090x(00)00026-7

Cited by 514 publications

(292 citation statements)

References 13 publications

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“…White et al (2000) describe several algorithms (or parts of algorithms) for matching an estimated position to a network representation of the street system and attempt to evaluate four of them. Since most route changes occur at intersections, their study suggests that particular attention needs to be paid to the problems that arise at intersections.…”

Section: Existing Literature On Map Matchingmentioning

confidence: 99%

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A general map matching algorithm for transport telematics applications

et al. 2003

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Section: Existing Literature On Map Matchingmentioning

confidence: 99%

“…This technique is known as map matching (MM). A formal definition of MM can be found in Bernstein and Kornhauser (1996), White et al (2000) and Greenfeld (2002). The most complex algorithm is the general MM algorithm that does not assume any knowledge or any other information regarding the expected location of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

A general map matching algorithm for transport telematics applications

et al. 2003

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“…While initially such a problem involved time and energy resources due to the necessity for manual manipulation (Xiong, 2000;White et al, 2000), several improvements have since been introduced by means of the capabilities of GIS and of ITS, as described by Quddus et al (2007). The map matching issue is very important whenever the integration between GIS representation and GPS devices is considered (Taylor et al, 2000;Mintsis et al, 2004;Byon et al, 2007).…”

Section: 1geodatabase Framework In Transportation Researchmentioning

confidence: 99%

Geo-ICT in Transportation Science

Borzacchiello¹,

Casas

Ciuffo³

et al. 2009

Geospatial Technology and the Role of Location in Science

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Since the first appearances of Geographical Information Systems (GIS), transportation science has found in them its natural support, to represent, first, the spatial and, later on, the temporal aspects of transportation networks and infrastructures. As GIS developed so did their use in transportation science, and they became essential not only in visualization but also to facilitate and speed data management, algorithmic operations, and decision making. This paper analyses the contributions and influence of GIS in transport science on the basis of three frameworks: geodatabase, geomapping, and geomodelling, all of which highlight the importance of location.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

Indoor trajectory identification: Snapping with uncertainty

Wang

Shroff

Zha

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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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

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Some map matching algorithms for personal navigation assistants

Cited by 514 publications

References 13 publications

A general map matching algorithm for transport telematics applications

A general map matching algorithm for transport telematics applications

Geo-ICT in Transportation Science

Indoor trajectory identification: Snapping with uncertainty

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