With the widespread adoption of smartphones and IoT technologies, the demand for location-based services has grown significantly. However, positioning accuracy often fails to meet application requirements due to signal interference from architectural obstructions and other factors. To address the non-line-of-sight (NLOS) errors in ultra-wideband (UWB) and the error accumulation in pedestrian dead reckoning (PDR), this paper proposes a tightly coupled PDR/UWB indoor positioning method with NLOS adaptive correction. By integrating extended Kalman filtering (EKF), the method eliminates pedestrian height parameters through coplanar base station projection and adaptively corrects NLOS ranging errors using chi-square testing. Experimental validation in both non-occluded and occluded underground parking environments demonstrates that, compared to standalone UWB positioning, the loosely coupled and tightly coupled algorithms improve positioning accuracy by 41.28% and 44.84% in non-occluded conditions and by 36.52% and 42.89% in occluded conditions, respectively. The results indicate that the tightly coupled algorithm achieves higher precision and exhibits superior robustness in complex environments, providing an effective solution for high-accuracy indoor positioning.

A robust back-end module with loop closure detection is crucial for accurate positioning and mapping in LiDAR-based simultaneous localization and mapping (SLAM) systems, particularly in Internet of Things (IoT) environments where multiple devices collaborate. Traditional methods that rely on images or point clouds often fail in environments with similar structures or textures, leading to incorrect loop closures. To address this, we propose a novel LiDAR SLAM system that integrates a front-end odometry module, a loop closure detection module using text semantics and geometric constraints, and a global optimization module. By using cameras on an unmanned ground vehicle (UGV), the system captures text information from the environment, enabling semantic matching to identify potential loops. Geometric constraints help eliminate erroneous loops caused by identical text in different locations. Evaluations on datasets with similarly structured environments, such as indoor parking lots, outdoor campus areas, and mixed indoor–outdoor scenes, show that our method significantly improves loop closure detection accuracy and global precision compared to existing state-of-the-art approaches. Our research can support autonomous IoT systems and multiagent systems that rely on accurate positioning and mapping, with potential applications in embodied intelligence, self-driving cars, and smart cities.

Reliable indoor localization is crucial for location-based services.Unlike outdoor environments where the Global Navigation Satellite System (GNSS) is prevalent, indoor localization systems employ diverse methods to enhance the accuracy of individual devices. However, these methods face limitations, such as the dependence on pre-existing map data and the necessity of installing anchors. The advancement of the Internet of Things (IoT) and the increasing availability of smart devices have enabled the development of more flexible and dynamic indoor localization solutions. In this paper, we propose a novel method to enhance indoor localization through cooperative localization framework. The core concept involves utilizing existing robots as mobile robot anchors to enhance pedestrian localization accuracy through interaction with pedestrians, particularly in environments lacking fixed anchors. We employed a factor graph optimization approach to tightly couple intra-device and inter-device data. This integration dynamically adjusts the inclusion of anchor data based on its quality, thereby minimizing error propagation. The experimental results demonstrate that the localization accuracy of our proposed method better than extend Kalman filter algorithms, emphasizing the potential of mobile IoT devices in indoor localization systems.

Location-based services (LBS) have become an integral part of daily life and work for the general public. However, achieving widespread and accurate positioning in typical indoor environments remains a significant challenge, particularly in multi-floor indoor parking lots where radio frequency signals like WiFi are often unavailable. Indoor magnetic matching presents a viable solution, but it requires reducing mapping costs through the use of crowdsourced data. To tackle this issue, we propose an innovative method for constructing magnetic maps using crowdsourced vehicle data. Our approach introduces a multi-user joint vehicle dead reckoning technique based on graph optimization, which provides consistent directional estimates of crowdsourced vehicle trajectories. Subsequently, we establish associations between different vehicle trajectories using multi-attribute features of the magnetic field. Building on this foundation, we propose a global trajectory optimization with inequality and equality constraints to achieve precise estimation of crowdsourced vehicle trajectories. Testing with simulated data from two three-floor underground parking lots demonstrates that the proposed method, utilizing only on-board smartphone sensor data, achieves plane and elevation errors of less than 2.75 meters (95%) and 0.59 meters (95%), respectively. Additionally, the magnetic matching positioning error based on crowdsourced magnetic sequence maps is less than 2.29 meters (95%).

The high risks in tunnel construction underscore the critical necessity for intelligent tunnel construction. Unmanned tunnel data collection is vital for intelligent construction; additionally, semantic segmentation aids in understanding the environment. However, complex tunnel terrains are challenging for three-dimensional (3D) laser scanning, and diverse interior structures and nontunnel elements complicate accurate segmentation by subsequent networks. Therefore, this paper proposed a tunnel mobile 3D mapping system (TMMS) for complex terrain in construction tunnels using a quadruped robot and simultaneous localization and mapping (SLAM). Additionally, a deep learning-based semantic segmentation network (TUC-Net) is proposed for analysing 3D point clouds in tunnels under construction. The research presented the neighbourhood feature perception enhancement (NFPE) module to enhance the representation of local features, introduce a self-attention (SA) module and improve the loss function to improve network accuracy. The NFPE module enhances feature aggregation for unstructured objects, and the SA module improves the learning of global features, critical for tunnel point cloud segmentation. The TMMS is used to collect point cloud data from tunnels under construction, leading to the creation of the tunnels under construction point clouds (TUCPC) dataset for training and evaluating the TUC-Net network. Compared to other 3D point cloud semantic segmentation methods, the proposed method demonstrated superior performance, achieving an overall accuracy (OA) of 99.45% and a mean intersection over union (mIoU) of 94.06%, surpassing that of other methods by at least 4.41%. In addition, ablation studies were also performed on the NFPE and SA modules to validate their efficacy.

Social distancing and contact tracing are effective nonpharmaceutical means to ensure public safety and control the rapid spread of infectious diseases. Internet of Things (IoT) sensors can provide reliable data sources for contact tracing, especially in urban public areas. However, existing contact tracing studies mainly use 2-D coordinates or distances to detect direct contact between people and lack indirect contact behavior modeling and sensing in urban 3-D environments. Additionally, an efficient storage and spatiotemporal search method is required to find unsafe contact cases from the large amount of data collected by IoT sensors. This article proposes an innovative spatiotemporal detection and tracing framework for both direct and indirect contact behavior using multicamera sensors. A multicamera coordinate conversion model (M-CCM) is designed to achieve camera calibration and 3-D trajectory aggregation based on a spatiotemporal constraint strategy. Using the aggregated 3-D trajectories, this method further defines several fine-grained characteristics of both direct and indirect contact behavior. A contact graph is designed to model and represent contact activities, which supports efficient spatiotemporal searching and tracing of unsafe contact activities. We have verified the performance of the proposed framework using both a public data set and our data set. Experiments demonstrate that the 3-D trajectory coordinate conversation accuracy was 0.2 m using the proposed M-CCM. The social distance and contact time detection precision and recall are 80% and 93%, respectively, for close contact (<1.5 m). Furthermore, the contact graph can reduce the memory space for monitoring data storage and support efficient spatiotemporal contact tracing.

Visual perception technology is an important means to facilitate safe navigation for visually impaired people based on Internet of Things (IoT)-enabled camera sensors. However, due to the rapid development of urban traffic systems, traveling outdoors is becoming increasingly complicated. Visually impaired individuals must implement different types of tasks simultaneously, such as finding roads, avoiding obstacles, and viewing traffic lights, which is challenging for both them and navigation assistance methods. To solve these problems, we propose a multitask visual navigation method for visually impaired individuals using an IoT-based camera. A lightweight neural network is designed, which adopts a multitask learning architecture to perform scene classification and path detection tasks simultaneously. We propose two modules, i.e., an enhanced inverted residuals (EIR) block and a lightweight Vision Transformer (ViT) block (LWVIT block), to effectively combine the properties of convolutional neural networks (CNNs) and ViT networks. The two modules allow the network to better learn local features and global representations of images while remaining lightweight. The experimental results show that the proposed method can achieve these tasks simultaneously in a lightweight manner, which is important for IoT-based navigation applications. The accuracy of our method in scene classification reaches 91.7%. The path direction and endpoint detection errors are 6.59∘ and 0.09, respectively, for blind road and 6.81∘ and 0.06, respectively, for crosswalk. The number of parameters of our method is 0.993 M, which is smaller than that of the comparison methods. An ablation study further demonstrates the effectiveness of the proposed method.

To improve the accuracy and efficiency of LiDAR mapping, cooperative simultaneous localization and mapping (SLAM) has been considered for complex large scenes. Recognizing the same positions and detecting global loop closures are important for achieving cooperative SLAM. However, most of the current position recognition and loop closure detection methods are based on images or point clouds. These methods may make mistakes if structures or textures are similar. To overcome this problem, we propose SemanticCSLAM, which is a Cooperative SLAM system that uses environment semantic landmarks for position recognition and loop closure detection. The proposed SemanticCSLAM consistes of a single SLAM module based on A-LOAM, a trajectory alignment module and a global optimization module based on environment landmarks. Through the inertial measurement unit (IMU) carried by the agent, such as an unmanned ground vehicle (UGV), the environment landmarks can be detected. Based on these environment landmarks, the alignment module aligns trajectories from different agents. Finally, the loop closure detection and optimization module performs loop closure detection and global optimization based on these environment landmarks. We collected a dataset, which contains indoor and outdoor data, for testing. These experimental results in different scenes show that the environment landmarks can effectively improve the performance of cooperative SLAM systems.

Thermal images capture temperature information of the environments instead of texture, making it well suitable for obtaining position in dark environments. Many methods have been proposed to handle RGB images, while thermal image-based localization methods are not well studied. To address it, we propose DarkLoc+, a thermal image-based indoor localization method based on the attention model and relative constraints between images under a learning-based localization framework. To be specific, we utilize self-attention to extract reprehensive features from thermal images and exploit relative constraints to enforce the convolutional neural networks (CNNs) to predict global poses. Relative pose loss(RelLoss)and relative regression loss are designed to work with global poses to constrain the network in feature and pose space simultaneously. We evaluate the proposed method on the public thermal images indoor dataset and our own dataset. The experimental results demonstrate that our method can obtain accurate position information.

High-quality 3D point cloud maps are essential for precise indoor environments modeling. However, constructing such maps in multi-storey indoor environments is challenging due to the presence of narrow non-structural spaces, such as staircases, corners, and corridors with similar textures. Simultaneous localization and mapping (SLAM) in these scenes is particularly difficult, as cumulative errors can lead to incorrect loop closures and drastic degradation in map quality. To address these challenges. This paper proposed a SLAM method base on multiple ground constraints pose optimization (MGCPO) which uses a backpack LiDAR system. The proposed method includes two novel modules. The first, a regression analysis-based scenarios recognition (RASR) module provides a reference for the construction of ground constraints. The second, based on different scene detection results, the MGCPO module constrains the sensor pose using the floor plane to reduce localization errors and effectively decrease loop closure detection errors. Qualitative experiments demonstrate that our proposed method outperforms state-of-the-art methods in challenging scenarios. Quantitative experiments show that our method achieves an error rate of just 1.06% using only LiDAR sensors.

Image-based localization is an essential component for many applications such as autonomous driving, virtual reality. Many researchers focus on developing methods for daytime via RGB images. Few research study the methods for night condition. The main reason is that the RGB-based image localization methods fail to work in dark scenes due to the low illumination. Thermal images capture temperature information instead of texture, making it well suitable for dark environments. However, thermal image-based localization methods are not well studied. To address it, we propose an attention-based localization method for night condition (DarkLoc) using thermal images. The proposed method introduce the attention mechanism in a deep learning-based framework to extract key information from low quality thermal images. The attention model can enforce the whole network focus on geometry meaningful feature in thermal images and thus improve the localization accuracy. We perform extensive experiment on the thermal image dataset. The results show that the attention model can enforce the whole network to learn geometry meaningful feature from thermal images and effectively locate the thermal image in real-time.

As the city continues to grow, the demand for positioning in large urban buildings and underground spaces has become particularly important. However, due to the obstruction by walls and other structures, satellite signals are subject to extremely strong interference and attenuation indoors, so the indoor positioning accuracy is relatively low compared with outdoor positioning. And with the development of MEMS sensors, it is possible to use inertial navigation on mobile devices. Because of the cumulative error of the integration process, there are problems in using traditional inertial positioning methods on smartphones. In this paper, we presented a deep learning based pedestrian and vehicle Indoor positioning method, XDRNet. This method can get the positioning trajectories of moving objects by learning the relationship between real trajectories and motion states. Then, driving behavior and pedestrian behavior are distinguished by deep neural network method. In this paper, a lightweight network structure is used to make the above method work better on smartphones. Based on the above research, this paper uses the underground parking area of an office building as the experimental area to evaluated the positioning performance of above methods. The experimental results have demonstrated the superiority of the methods in this paper, which is applicable to both pedestrian and vehicle motion carriers.

Missing data are critical deficiency in the investigation of displacement measurement in urban Internet of Things system. In the insight of recovering missing displacement data, this article presents a data-driven and high-dimensional gap-imputation method, Tucker decomposition with L2 regularization. Results on the global navigation satellite system (GNSS) time series collected from an intelligent structural health monitoring system show that the recovery accuracy is improved compared with some popular benchmark methods. When the missing rate is 50%, compared with singular spectrum analysis, singular value decomposition, CP optimization algorithm,$k$-nearest neighbors, and Tucker decomposition via alternating least squares, Tucker decomposition with L2 regularization can improve the average mean absolute error by about 4.74, 4.95, 5.82, 2.29, and 5.67 mm for all locations. It can be concluded that the consideration of multiple temporal correlations is necessary for missing data imputation. Compared with matrix decomposition, tensor decomposition can improve the ability for high-dimensional correlations in the GNSS time series.

With the development of quadrotor-based location services, accurate indoor quadrotor localization plays an important role in various applications. Tight fusion refers to the process of integrating multisensor data into state estimation for optimization, and finally obtaining pose information. In this article, we propose a novel tight fusion method for quadrotor localization by fusing the WiFi round-trip time (RTT) and built-in smartphone microelectromechanical sensors. Unlike existing 3-D localization frameworks, the key contribution of the proposed method is to integrate 3-D outlier detection, state estimation, coordinate frame alignment, and data fusion into a nonlinear filtering framework. Specifically, this method is divided into four main steps: 1) the coordinates of the mobile phone and the quadrotor are converted to the same coordinate system through the coordinate alignment method we propose; 2) the proposed outlier detection method is used to obtain the 3-D coordinates of the quadrotor based on WiFi RTT; 3) the WiFi RTT localization results are integrated into an error-state Kalman filter (ESKF) to perform the integrated localization of the quadrotors; and 4) a Rauch–Tung–Striebel (RTS) smoothing method is used to optimize the localization results. The experimental results demonstrate that the proposed method outperforms the classic localization method in terms of both accuracy and robustness.

With the promotion of low-carbon travel, pedestrian network plays an important role in many location-based applications, such as pedestrian navigation and refined traffic management. Due to the lack of systematic data acquisition mechanics, the accuracies and detail levels of pedestrian network data are hardly capable of satisfying the demands of such transportation applications. Presently, various mobile phone apps recorded and stored users' movement trajectories, which provide a valuable data source for pedestrian network construction. Hence, this article proposes a crowdsourcing-based system for generating pedestrian network that encompasses three key components of crowdsourced walking trajectory data filtering, pedestrian network construction and evaluation of pedestrian network. Self-collected data and open platform data were used to evaluate the proposed system. Experimental results demonstrate that the proposed method can accurately and completely extract pedestrian network. Moreover, the pedestrian network can be updated in a timely manner by the proposed method. The data collection application and the collected data are available to the public.

Spatial data visualization technology allows users to understand Geographic Information System (GIS) applications. Unlike traditional visualization methods, Augmented Reality (AR) inserts virtual objects and information directly into digital representations of the real world, which makes these objects and data more easily understood and interactive. However, effective AR-GIS systems and rich spatial information visualization is still a challenging task. In addition, indoor AR-GIS systems are further impeded by the limited capacity of these systems to detect and display geometry and semantic information. To address this problem, a novel AR and indoor map fusion method is proposed that automatically registers spatial information onto a live camera view of a mobile phone. We fused Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) localization techniques to track the camera positions. The proposed algorithm extracts and matches a bounding box of the indoor map to a real world scene. We render the indoor map and semantic information into the real world, based on the real-time computed spatial relationship between the indoor map and live camera view. Experimental results demonstrate that our approach accurately and richly visualizes spatial information. Our augmented reality and indoor map fusion technique effectively links rich indoor spatial information to real world scenes in AR integrated GIS.

3-D visualization of location-based services (LBSs) in a geographic information system (GIS) and the Internet-of-Things (IoT) applications could enhance the user experience. Texture is generally the most noticeable visual element in a 3-D indoor model. However, it usually tends to be outdated and becomes inaccurate in complex and dynamic environments. The applications of such 3-D visualizations, especially in indoor environments, are further impeded by a limited capacity to represent frequently changing scenes due to object displacements. To overcome this problem, a novel location and 3-D visual awareness-based method is proposed to automatically register and update a live image onto a 3-D indoor model. In this method, a multisensor fusion-based indoor localization algorithm employs an indoor map to integrate multisource data, including activity detection information, pedestrian dead reckoning (PDR), and 3-D vision-based localization data. Based on the positioning results, we locate the region of the photograph capture within a scene and identify the triangular meshes of the facing wall. The texture is extracted from real-time images through a 3-D indoor understanding technique and automatically fused with the indoor 3-D model. The geometric relationship between texture and the 3-D indoor model is established by calculating the UV coordinates. The experimental results demonstrate that our approach can accurately update the dynamic indoor model textures regardless of the scene. Based on this texture updating technique, users experience an enhanced presentation of LBS information on our 3-D visualization platform.

Online-to-offline (O2O) commerce connecting service providers and individuals to address daily human needs is quickly expanding. In particular, on-demand food, whereby food orders are placed online by customers and delivered by couriers, is becoming popular. This novel urban food application requires highly efficient and scalable real-time delivery services. However, it is difficult to recruit enough couriers and route them to facilitate such food ordering systems. This paper presents an online crowdsourced delivery (OCD) approach for on-demand food. Facilitated by Internet-of-Things and 3G/4G/5G technologies, public riders can be attracted to act as crowdsourced workers delivering food by means of shared bicycles or electric motorbikes. An online dynamic optimization framework comprising order collection, solution generation, and sequential delivery processes is presented. A hybrid metaheuristic solution process integrating the adaptive large neighborhood search and tabu search approaches is developed to assign food delivery tasks and generate high-quality delivery routes in a real-time manner. The crowdsourced riders are dynamically shared among different food providers. Simulated small-scale and real-world large-scale on-demand food delivery instances are used to evaluate the performance of the proposed approach. The results indicate that the presented crowdsourced food delivery approach outperforms traditional urban logistics. The developed hybrid optimization mechanism is able to produce high-quality crowdsourced delivery routes in less than 120 s. The results demonstrate that the presented OCD approach can facilitate city-scale on-demand food delivery.

This paper first proposes a new clustering algorithm for selection of reference points (RPs) based on virtual positions of access points for indoor localization in area without linear constraints, which can not only cluster automatically but also guarantee the consistency of methods between the offline phase clustering and the online phase positioning. A new weighted algorithm based on physical distance is then presented for position determination. With angle velocity measurement provided such as by gyroscope, the weighted algorithm is particularly suited for scenarios where the mobile moves along a trajectory. The number of clusters in traditional RP clustering algorithms needs to be predefined, which means an unsuitable number of clusters would lead to poor estimation accuracy. Traditional weighted K-nearest neighbor (WKNN) algorithm weights the RPs' coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of Wi-Fi RSSI. Experimental results show that the proposed weighted algorithm considerably outperforms the K-nearest neighbor (KNN), Euclidean-WKNN, ManhattanWKNN, EWKNN, LiFS, and GPR in terms of positioning accuracy which is defined as the cumulative distribution function of position error. The results also demonstrate that RPs in indoor area without linear constraints can be clustered automatically by the proposed clustering algorithm, and cumulative distribution function of the proposed clustering algorithm outperforms KNN, WKNN, RP location clustered, and signal distance clustered.

This paper presents an activity sequence-based indoor pedestrian localization approach using smartphones. The activity sequence consists of several continuous activities during the walking process, such as turning at a corner, taking the elevator, taking the escalator, and walking stairs. These activities take place when a user walks at some special points in the building, like corners, elevators, escalators, and stairs. The special points form an indoor road network. In our approach, we first detect the user's activities using the built-in sensors in a smartphone. The detected activities constitute the activity sequence. Meanwhile, the user's trajectory is reckoned by Pedestrian Dead Reckoning (PDR). Based on the detected activity sequence and reckoned trajectory, we realize pedestrian localization by matching them to the indoor road network using a Hidden Markov Model. After encountering several special points, the location of the user would converge on the true one. We evaluate our proposed approach using smartphones in two buildings: an office building and a shopping mall. The results show that the proposed approach can realize autonomous pedestrian localization even without knowing the initial point in the environments. The mean offline localization error is about 1.3 m. The results also demonstrate that the proposed approach is robust to activity detection error and PDR estimation error.

Indoor maps are integral to pedestrian navigation systems, an essential element of intelligent transportation systems (ITS). In this paper, we propose ALIMC, i.e., Activity Landmark-based Indoor Mapping system via Crowdsourcing. ALIMC can automatically construct indoor maps for anonymous buildings without any prior knowledge using crowdsourcing data collected by smartphones. ALIMC abstracts the indoor map using a link-node model in which the pathways are the links and the intersections of the pathways are the nodes, such as corners, elevators, and stairs. When passing through the nodes, pedestrians do the corresponding activities, which are detected by smartphones. After activity detection, ALIMC extracts the activity landmarks from the crowdsourcing data and clusters the activity landmarks into different clusters, each of which is treated as a node of the indoor map. ALIMC then estimates the relative distances between all the nodes and obtains a distance matrix. Based on the distance matrix, ALIMC generates a relative indoor map using the multidimensional scaling technique. Finally, ALIMC converts the relative indoor map into an absolute one based on several reference points. To evaluate ALIMC, we implement ALIMC in an office building. Experiment results show that the 80th percentile error of the mapping accuracy is about 0.8-1.5 m.