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Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS / Fabian Hölzke in Geo-spatial Information Science, vol 22 n° 2 (June 2019)  

Public [article]  inGeo-spatial Information Science > vol 22 n° 2 (June 2019) . - pp 114 - 127 Titre : Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS Type de document : Article/Communication Auteurs : Fabian Hölzke, Auteur ; Johann-P. Wolff, Auteur ; Frank Golatowski, Auteur ; Christian Haubelt, Auteur Année de publication : 2019 Article en page(s) : pp 114 - 127 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] carte d'intérieur [Termes IGN] données GPS [Termes IGN] navigation à l'estime [Termes IGN] navigation pédestre [Termes IGN] positionnement en intérieur Résumé : (Auteur) Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change. Pedestrian Dead Reckoning (PDR) applies this concept to walking persons. The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered. Here, the movement of a foot and the corresponding direction change is measured and summed up, to infer the current position. Measuring and integrating the corresponding physical parameters, e.g. using inertial sensors, introduces small errors that accumulate quickly into large distance errors. Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths. In this paper, we use building maps to improve localization based on a single foot-mounted inertial sensor. We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation. Even though the computation of individual steps is quite accurate, small errors still accumulate in the long term. We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks, such as orientation and displacement drift, and discuss restrictions and disadvantages of these algorithms. We also present a method of deriving the initial position and orientation from GPS measurements. We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building, walking each route three times. Our quantitative results show an endpoint accuracy improvement of up to 60% when using likely paths and 23% when using unlikely paths. However, both approaches can also decrease accuracy in certain scenarios. We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods. Numéro de notice : A2019-323 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2019.1617528 Date de publication en ligne : 30/05/2019 En ligne : https://doi.org/10.1080/10095020.2019.1617528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93323 [article]

A regression model-based method for indoor positioning with compound location fingerprints / Tomofumi Takayama in Geo-spatial Information Science, vol 22 n° 2 (June 2019)  

Public [article]  inGeo-spatial Information Science > vol 22 n° 2 (June 2019) . - pp 107 - 113 Titre : A regression model-based method for indoor positioning with compound location fingerprints Type de document : Article/Communication Auteurs : Tomofumi Takayama, Auteur ; Takeshi Umezawa, Auteur ; Nobuyoshi Komuro, Auteur ; Noritaka Osawa, Auteur Année de publication : 2019 Article en page(s) : pp 107 - 113 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] Bluetooth [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] navigation à l'estime [Termes IGN] positionnement en intérieur [Termes IGN] régression Résumé : (Auteur) This paper proposed and evaluated an estimation method for indoor positioning. The method combines location fingerprinting and dead reckoning differently from the conventional combinations. It uses compound location fingerprints, which are composed of radio fingerprints at multiple points of time, that is, at multiple positions, and displacements between them estimated by dead reckoning. To avoid errors accumulated from dead reckoning, the method uses short-range dead reckoning. The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11 × 5 m with furniture inside. The Received Signal Strength Indicator (RSSI) values of the beacons were collected at 30 measuring points, which were points at the intersections on a 1 × 1 m grid with no obstacles. A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them. Random Forests (RF) was used to build regression models to estimate positions from location fingerprints. The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons. This error is lower than that received with a single-point baseline model, where a feature vector is composed of only RSSI values at one location. The results suggest that the proposed method is effective for indoor positioning. Numéro de notice : A2019-324 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2019.1612599 Date de publication en ligne : 17/05/2019 En ligne : https://doi.org/10.1080/10095020.2019.1612599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93324 [article]

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones / Catia Real Ehrlich in Geo-spatial Information Science, vol 22 n° 2 (June 2019)  

Public [article]  inGeo-spatial Information Science > vol 22 n° 2 (June 2019) . - pp 73 - 88 Titre : Indoor localization for pedestrians with real-time capability using multi-sensor smartphones Type de document : Article/Communication Auteurs : Catia Real Ehrlich, Auteur ; Jörg Blankenbach, Auteur Année de publication : 2019 Article en page(s) : pp 73 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] altitude barométrique [Termes IGN] Bluetooth [Termes IGN] méthode de Monte-Carlo [Termes IGN] microsystème électromécanique [Termes IGN] navigation à l'estime [Termes IGN] navigation pédestre [Termes IGN] positionnement en intérieur [Termes IGN] téléphone intelligent Résumé : (Auteur) The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented. Numéro de notice : A2019-325 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2019.1613778 Date de publication en ligne : 21/05/2019 En ligne : https://doi.org/10.1080/10095020.2019.1613778 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93327 [article]