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Solving Monocular Visual Odometry Scale Factor with Adaptive Step Length Estimates for Pedestrians Using Handheld Devices

1
Institut Français des Sciences et Technologies des Transports, de l’Aménagement et des Réseaux (IFSTTAR) AME GEOLOC, 44340 Bouguenais, France
2
Centrale Nantes, 44300 Nantes, France
3
Institut de Recherche en Sciences et Techniques de la Ville (IRSTV), 44300 Nantes, France
4
Laboratory for Image and Media Understanding (LIMU), Kyushu University, Fukuoka 819-0395, Japan
5
Centre de Recherche Nantais Architectures Urbanités (CRENAU) AAU, 44262 Nantes, France
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 953; https://doi.org/10.3390/s19040953
Received: 29 December 2018 / Revised: 25 January 2019 / Accepted: 18 February 2019 / Published: 23 February 2019
(This article belongs to the Special Issue Sensor Fusion and Novel Technologies in Positioning and Navigation)
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Abstract

The urban environments represent challenging areas for handheld device pose estimation (i.e., 3D position and 3D orientation) in large displacements. It is even more challenging with low-cost sensors and computational resources that are available in pedestrian mobile devices (i.e., monocular camera and Inertial Measurement Unit). To address these challenges, we propose a continuous pose estimation based on monocular Visual Odometry. To solve the scale ambiguity and suppress the scale drift, an adaptive pedestrian step lengths estimation is used for the displacements on the horizontal plane. To complete the estimation, a handheld equipment height model, with respect to the Digital Terrain Model contained in Geographical Information Systems, is used for the displacement on the vertical axis. In addition, an accurate pose estimation based on the recognition of known objects is punctually used to correct the pose estimate and reset the monocular Visual Odometry. To validate the benefit of our framework, experimental data have been collected on a 0.7 km pedestrian path in an urban environment for various people. Thus, the proposed solution allows to achieve a positioning error of 1.6–7.5% of the walked distance, and confirms the benefit of the use of an adaptive step length compared to the use of a fixed-step length.
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Keywords: pose estimation; localization; handheld device; pedestrian navigation; urban mobility; augmented reality pose estimation; localization; handheld device; pedestrian navigation; urban mobility; augmented reality
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Antigny, N.; Uchiyama, H.; Servières, M.; Renaudin, V.; Thomas, D.; Taniguchi, R.-I. Solving Monocular Visual Odometry Scale Factor with Adaptive Step Length Estimates for Pedestrians Using Handheld Devices. Sensors 2019, 19, 953.

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