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An integrated navigation system is a promising solution to improve positioning performance by complementing estimated positioning in each sensor, such as a global positioning system (GPS), an inertial measurement unit (IMU), and an odometer sensor. However, under GPS-disabled environments, such as urban canyons or tunnels where the GPS signals are difficult to receive, the positioning performance of the integrated navigation system decreases. Therefore, deep learning-based integrated navigation systems have been proposed to ensure seamless localization under various positioning conditions. Nevertheless, the conventional deep learning-based systems are applied with a lack of consideration of context features on surface condition, wheel slip, and movement pattern, which are factors causing positioning performance. In this paper, a context-aware integrated navigation system (CAINS) is proposed to ensure seamless localization, especially under GPS-disabled conditions. In the proposed CAINS, two deep learning layers are designed with context-aware and state estimation layers. The context-aware layer extracts vehicle context features from IMU data, while the state estimation layer predicts the GPS position increments by modeling the relationship between context features, velocity, attitude, and position increments. From simulation results, it is confirmed that the positioning accuracy can be significantly improved based on the proposed CAINS when compared with conventional navigation systems.

The Simultaneous Localization and Mapping (SLAM) technology is fundamental to the autonomous navigation of Unmanned Aerial Vehicles (UAVs) and holds significant value for the realization of UAV-based bridge inspections. However, conventional SLAM methods for UAV face challenges related to low continuity and weak reliability across different scenes, making it difficult to meet the requirements for comprehensive bridge localization and mapping. To address the limitations of existing UAV-based SLAM approaches, we propose a seamless SLAM system that integrates IMU, LiDAR, and RTK. In open scenes (such as the top and sides of a bridge), high-precision absolute localization is achieved by fusing IMU and RTK through an iterative error-state Kalman filter (IESKF). In occluded environments (such as the underside of a bridge), an IMU/LiDAR odometry is used to recursively estimate the UAV’s pose. In cross-scene situations (when the UAV passes through a bridge arch), the quality of sensor data is evaluated based on an interactive multi-model (IMM), and an adaptive switching mechanism is employed between two localization modes—IMU/RTK mode and IMU/LiDAR mode—ensuring smooth and seamless multi-source fusion localization even in the presence of sensor signal fluctuations. To validate the effectiveness of our method, extensive tests were conducted on several real-world bridge scenarios. The results show that our method can achieve centimetre-level cross-scene localization accuracy in bridge inspection applications, which indicates its feasibility and effectiveness.

Uncertainty modelling is regarded as one of the core components in the field of urban navigation, that can affect the performance of indoor and outdoor location information acquisition, especially under obscured scenarios. Existing multi-source fusion-based seamless positioning algorithms are subjected to random and highly dynamic human motion characteristics and changeable observation errors caused by the dynamic occlusions of human and buildings under urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of final multi-source fusion structure. To fill in this gap, this paper proposes an enhanced seamless localization framework using spatial-temporal uncertainty predictor under obscured indoor and outdoor scenes (ESL-STUP), that takes into account both temporal correlation and spatial correlation of trajectories provided by Wi-Fi, GNSS, and sensor-originated motion information. An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. Comprehensive experiments indicate that compared with existing multi-source fusion-based seamless positioning structure, the proposed ESL-STUP realizes much better performance under different scenes.

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

We present a workflow for seamless real-time navigation and 3D thermal mapping in combined indoor and outdoor environments in a global reference frame. The automated workflow and partly real-time capabilities are of special interest for inspection tasks and also for other time-critical applications. We use a hand-held integrated positioning system (IPS), which is a real-time capable visual-aided inertial navigation technology, and augment it with an additional passive thermal infrared camera and global referencing capabilities. The global reference is realized through surveyed optical markers (AprilTags). Due to the sensor data’s fusion of the stereo camera and the thermal images, the resulting georeferenced 3D point cloud is enriched with thermal intensity values. A challenging calibration approach is used to geometrically calibrate and pixel-co-register the trifocal camera system. By fusing the terrestrial dataset with additional geographic information from an unmanned aerial vehicle, we gain a complete building hull point cloud and automatically reconstruct a semantic 3D model. A single-family house with surroundings in the village of Morschenich near the city of Jülich (German federal state North Rhine-Westphalia) was used as a test site to demonstrate our workflow. The presented work is a step towards automated building information modeling.

The Global Navigation Satellite System (GNSS) is commonly used for outdoor positioning. However, its effectiveness diminishes in urban canyons and indoor environments attributed to signal blockage. This study aims to explore the potential of GNSS signals penetrating indoor spaces through windows and to enhance indoor positioning with Three-Dimensional Mapping-Aided (3DMA) GNSS, a concept generally applied outdoors. The research employs a 3D model of a corridor with manually labeled window locations to predict satellite visibility within indoor areas. The study integrates Pedestrian Dead Reckoning (PDR) with an indoor Shadow-matching (I-SM) technique, utilizing an Extended Kalman Filter (EKF) to improve positioning accuracy. One of the findings indicates that the proposed method significantly enhances positioning performance and its availability, achieving a root mean square error (RMSE) that is 2 m better than using PDR alone or single epoch I-SM. The study concludes that integrating GNSS with I-SM technique and PDR can optimize an indoor positioning solution and highlights the potential for improved navigation solutions in complex urban environments.

With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of low localization accuracy in the I/O transition area and the difficulty of achieving fast and accurate I/O switching, a Kalman filter based on the salp swarm algorithm (SSA) for seamless multi-source fusion positioning of global positioning system/inertial navigation system/smartphones (GPS/INS/smartphones) is proposed. First, an Android smartphone was used to collect sensor measurement data, such as light, magnetometer, and satellite signal-to-noise ratios in different environments; then, the change rules of the data were analyzed, and an I/O detection algorithm based on the SSA was used to identify the locations of users. Second, the proposed I/O detection service was used as an automatic switching mechanism, and a seamless indoor–outdoor localization scheme based on improved Kalman filtering with K-L divergence is proposed. The experimental results showed that the SSA-based I/O switching model was able to accurately recognize environmental differences, and the average accuracy of judgment reached 97.04%. The localization method achieved accurate and continuous seamless navigation and improved the average localization accuracy by 53.79% compared with a traditional GPS/INS system.

Ubiquitous and seamless indoor-outdoor (I/O) localization is the primary objective for gaining more user satisfaction and sustaining the prosperity of the location-based services (LBS) market. Regular users, on the other hand, may be unaware of the impact of activating multiple localization sources on localization performance and energy consumption, or may lack experience deciding when to enable or disable localization sources in different environments. Consequently, an automatic handover mechanism that can handle these decisions on a user’s behalf can appreciably improve user satisfaction. This study introduces an enhanced I/O environmental awareness service that provides an automated handover mechanism for seamless navigation based on multi-sensory navigation integration schemes. Moreover, the proposed service utilizes low-power consumption sensor (LPCS) indicators to execute continuous detection tasks and invoke GNSS in confusion scenarios, and transition intervals to make the most firm decision on the credibility of the LPCS-triggered transition and compensate for indicator thresholds. In this manner, GNSS are used for short intervals that help reduce detection latency and power consumption. Consequently, the proposed service guarantees accurate and reliable I/O detection while preserving low power consumption. Leveraging the proposed service as an automated handover helped realize seamless indoor-outdoor localization with less switching latency, using an integrated solution based on extended Kalman filter. Furthermore, the proposed energy-efficient service was utilized to confine crowdsourced data collection to the required areas (indoors and semi-indoors) and prevent excess data collection outdoors, thereby reducing power drainage. Accordingly, the negative impact of data collection on the user’s device can be mitigated, participation can be encouraged, and crowdsourcing systems can be widely adopted.

In the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention. No single positioning technology was capable of meeting the various positioning requirements in different environments. Selecting different positioning techniques for different environments is an alternative method. Detecting the users’ current environment is crucial for this technique. In this paper, we proposed to detect the indoor/outdoor environment automatically without high energy consumption. The basic idea was simple: we applied a machine learning algorithm to classify the neighboring Global System for Mobile (GSM) communication cellular base station’s signal strength in different environments, and identified the users’ current context by signal pattern recognition. We tested the algorithm in four different environments. The results showed that the proposed algorithm was capable of identifying open outdoors, semi-outdoors, light indoors and deep indoors environments with 100% accuracy using the signal strength of four nearby GSM stations. The required hardware and signal are widely available in our daily lives, implying its high compatibility and availability.

In this paper, a new emergency positioning technique is proposed based on ad hoc GNSS/UWB (Global Navigation Satellite System/Ultra-Wideband) network. The main innovations of the program are reflected in two aspects. First of all, a unified coordinate frame for indoor and outdoor environments is constructed dynamically with GNSS/UWB integration. In the outdoor environments, the high accuracy positioning can be achieved with GNSS/UWB equipment. The high-accuracy indoor coordinate is obtained by measuring the range observations between adjacent network nodes and outdoor GNSS/UWB nodes, and the range information of the UWB network is transmitted to the cloud server center. A network adjustment algorithm is proposed to improve the positioning accuracy of the UWB network. Secondly, a UWB indoor location algorithm based on robust EKF (Extended Kalman Filter) is proposed. By analyzing the transfer characteristics of gross error in EKF model, a new robust EKF model is established. The model is constructed based on the statistical characteristics of redundant observation components and prediction residual. The robust equivalent gain matrix is constructed, and the robust positioning solution of UWB is obtained with iteration. The global test is carried out first to further improve the real-time operation efficiency. Finally, a field indoor and outdoor seamless positioning experiment was carried out to verify the effectiveness of the proposed algorithm. The results show that the positioning accuracy of UWB emergency network nodes (anchors) can reach 0.35 m. Based on the network, the positioning accuracy of the tag can reach 0.38 m by applying the improved robust EKF positioning algorithm, which is improved by 20.83% and 73.43% compared with standard EKF and least square method, respectively.