Smartphones have become an integral part of daily human life and enable almost unlimited coverage of human mobility. Thus, collecting pervasive crowdsourced signatures is feasible. Autonomous localization of such signatures promotes the development of a self-deployable and ubiquitous Indoor Positioning System (IPS). However, previous IPSs-based crowdsourcing have not considered leveraging such data for developing ubiquitous IPSs. They have relied on methods for data selection and sources for localization adjustment that could work against realizing a ubiquitous system. In contrast, this study introduces a framework “Everywhere” that leverages crowdsourced data to develop a ubiquitous IPS and addresses existing challenges while developing such systems. Particularly, inertial data selection criteria are proposed to autonomously generate traces with better localization. Moreover, pervasive GNSS data are leveraged to adjust trace localization, while simultaneously introducing a deploying location (inside elevators) of one anchor node. The node surveys all the floors while reducing the localization error, especially for the buildings surrounded by GNSS-denied areas. Additionally, cumulative data densification is leveraged to realize pervasive resources within the building, thereby boosting trace adjustment and extending database spatial coverage. Furthermore, a better selection of neighboring fingerprints is proposed to enhance online fingerprinting. Such a framework can promote a ubiquitous IPS development for buildings regardless of whether they are surrounded by open sky or GNSS-denied areas.

In urban canyons, most of Global Navigation Satellite System (GNSS) signals are blocked or reflected by high-rising buildings. The lack of redundant fault-free measurements poses a great challenge for detecting and isolating faulty measurements, degrading the GNSS positioning accuracy, particularly in the cross-street direction. The current GNSS positioning accuracy in urban canyons is not sufficient for identifying the sides of a street, which is highly demanded by numerous pedestrian applications. To address this issue, this study proposes a sidewalk matching technique to improve the redundancy of fault-free measurements and achieve positioning accuracy at sidewalk level in urban canyons. The technique relies on a simple pedestrian map and smartphone sensors, without Three-Dimensional (3D) building models. A new algorithm is proposed to identify the sides of the street, and a searching algorithm is developed to find GNSS position under constraints of the sidewalk. Extensive evaluation of the proposed technique based on a real-time platform demonstrated that the accuracy of GNSS positioning was less than 5 m, enabling seamless determination of the street sides in urban canyons. The new sidewalk matching technique can be used in various real-life applications, including pedestrian collision avoidance systems, online ride-hailing service, and jaywalking monitoring.

Smartphone with multiple sensors integration has been widely used for navigation. The Inertial Measurement Unit (IMU) embedded in smartphones has been widely used for pedestrian navigation for counting steps. However, it is a challenge to measure the accurate velocity of the vehicle from the smartphone-embedded IMU data with a high noise level. Thus, current vehicle navigation with a smartphone relies substantially on the Global Navigation Satellite System (GNSS), which provides unreliable positions in urban dense areas due to the blockage and the reflection of GNSS signals. In this study, we propose a smartphone-based positioning method to improve vehicle positioning performance continuously in GNSS-degraded areas through the improvement of IMU velocity estimation. A convolutional neural network–Gated Recurrent Unit (CNN-GRU) combined deep learning odometry network, termed DeepOdo, is proposed to estimate the velocity of the vehicle with the IMU and barometer data as the input, rather than the traditional integral of the IMU measurements. Raw sensor data is utilized to boost the robustness. Labels of the DeepOdo are obtained from the integrated GNSS/IMU/Barometer solutions in the smartphone which significantly simplifies the dataset collection. In GNSS-denied areas, IMU, Barometer, and DeepOdo are integrated to provide accurate navigation solutions for the vehicle. Results of the proposed method show 73.14% and 98.33% improvements in horizontal and vertical directions respectively, compared with the Non-Holonomic Constraints (NHC) aided IMU. Finally, the DeepOdo network is deployed in Android smartphones to demonstrate that the proposed solution can work properly on the mobile platform.

This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart phone heading using MEMS measurements. Static and kinematic tests are performed, superiority of the improved heading algorithm is verified. We also present different heading processing solutions for comparison and analysis. Heading bias increases with time due to error accumulation and model inaccuracy. Thus, we develop a related heading calibration method based on beacons. This method can help correct smart phone headings continuously to decrease cumulative error. In addition to PDR, we also use GNSS and beacon measurements to integrate a fusion location. In the fusion procedure, we design related algorithms to adjust or limit the use of these different type observations to constrain large jumps in our Kalman filter model, thereby making the solution stable. Navigation experiments are performed in the streets of Mong Kok and Wanchai, which are typically the most crowded areas of Hong Kong, with narrow streets and many pedestrians, vehicles and tall buildings. The first experiment uses the strategy PDR + GNSS + beacon, in east–west orientation street, in which 10 m positioning error is improved from $30\%$ (smart phone internal GNSS) to $80\%$ and in south–north orientation street, in which 15 m positioning error is improved from $20\%$ (smart phone internal GNSS) to $80\%$ . The second experiment performs two long-distance tests without any beacons, in which the fusion scheme also has significant improvement, that is, 10 m positioning error is improved from $38\%$ to $60\%$ .

The performance of the Global Navigation Satellite Systems (GNSS) can be degraded significantly in urban areas and semi-outdoor environments due to the blockage and reflection of GNSS signals. To enhance smartphone positioning in these areas, the integration of Pedestrian Dead Reckoning (PDR) and Bluetooth Low Energy (BLE) sensors has been proposed and widely developed. However, the current Loosely Coupled (LC) integration algorithm often fails to achieve accurate positioning in complex urban environments. Challenges such as reliable assessment of GNSS quality, accurate estimation of pedestrian walking direction, and error due to variability of BLE signal strength across smartphones and accumulated by PDR remain to be addressed. To achieve precise and continuous location service in urban areas, we have explored the characteristics of BLE signals and integrated BLE with GNSS and PDR, enhancing positioning performance through three key algorithms. Firstly, GNSS quality is monitored with the aid of BLE signals and PDR results adaptively to minimize their impact on the integration results. Subsequently, the smartphone’s orientation and the heading precisely estimated from BLE signals are jointly utilized to determine the pedestrian’s walking direction. Finally, a Tightly Coupled (TC) Kalman filter, incorporating distance measurements from strong-signal BLE transmitters, is employed to tackle the device heterogeneity inherent in smartphone BLE sensors and to reduce the cumulative errors associated with PDR positioning. Extensive experiments on different smartphones demonstrate that the proposed system consistently achieves positioning accuracy within a 4-meter in both urban and semi-outdoor environments.