Accurate Positioning and Orientation Estimation in Urban Environment Based on 3D Models

Ghinamo, Giorgio; Corbi, Cecilia; Lovisolo, Piero; Lingua, Andrea Maria; Aicardi, Irene; Grasso, Nives

doi:10.1007/978-3-319-23222-5_23

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Several technologies can be used for indoor localization, such as wireless localization [ 1 , 2 , 3 , 4 ], sensor-based relative navigation ( i.e. , dead-reckoning) [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ], and image-based navigation [ 13 , 14 , 15 , 16 , 17 ]. Wireless localization has been widely used among these technologies.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Zhuang

Yang

et al. 2016

Sensors

393

230

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Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.

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Section: Introductionmentioning

confidence: 99%

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Zhuang

Yang

et al. 2016

Sensors

393

230

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“…Previous studies present a monocular visual SLAM system for global localization that exploits 2D cadastral map with an elevation model to localize the first key‐frame and to generate a sparse 2.5D georeferenced image database. Previous studies use prior knowledge of the 3D environment to build synthetic images that permit an estimation of the 3D position and 3D orientation.…”

Section: Related Workmentioning

confidence: 99%

Fusion of 3D GIS, Vision, Inertial and Magnetic Data for Improved Urban Pedestrian Navigation and Augmented Reality Applications

Antigny

Servières

Renaudin³

2018

J Inst Navig

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In the context of pedestrian navigation and Augmented Reality applications in urban environments, we propose to fuse the pose estimated through a vision process, thanks to a precisely known 3D model, with inertial and magnetic measurements. First, this allows for updating a Pedestrian Dead‐Reckoning process and improving the positioning accuracy. Second, a trusted pose estimate allows us to reproject 3D Geographical Information System content in Augmented Reality with qualified confidence. Because 3D Geographical Information System data are provided by many sources inducing an inhomogeneous precision and level of quality, being able to qualify these 3D contents is important to validate their relevance to use them. A long pedestrian path of 3 km in an urban environment with a sparsely known 3D model of urban furniture was conducted. This has permitted validation of the contribution of sensor fusion that improves the positioning accuracy and allows characterization of the 3D Geographical Information System content directly on‐site using Augmented Reality. Performance is presented in terms of positioning accuracy in urban spaces. © 2018 Institute of Navigation.

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“…First, we fuse the data from the magnetometer and gyroscope using the heading estimation algorithm presented by Wonho Kang in [21]. The fused heading angle is calculated as follows:…”

Section: Heading Determination By Fusing Gyroscope and Magnetometer Datamentioning

confidence: 99%

“…In addition, it is also well recognized that there exist many surveys on various positioning systems in the literature such as WiFi [13,14], UWB [15,16], RFID [17,18], Zigbee [19,20], and Image-Based Location (IBL) [21,22], among others. However, most of them require a fundamental facility to support their detection.…”

mentioning

confidence: 99%

An Improved PDR/Magnetometer/Floor Map Integration Algorithm for Ubiquitous Positioning Using the Adaptive Unscented Kalman Filter

Wang

et al. 2015

IJGI

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Abstract:In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU) and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET) algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR) model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP) is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian's trajectory, the Adaptive Unscented Kalman Filter (A-UKF) is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the 2639School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirm that the proposed scheme can reliably achieve meter-level positioning.

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Accurate Positioning and Orientation Estimation in Urban Environment Based on 3D Models

Cited by 5 publications

References 8 publications

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons

Fusion of 3D GIS, Vision, Inertial and Magnetic Data for Improved Urban Pedestrian Navigation and Augmented Reality Applications

An Improved PDR/Magnetometer/Floor Map Integration Algorithm for Ubiquitous Positioning Using the Adaptive Unscented Kalman Filter

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