With the development of society, autonomous mobile robots have become an important part of the Internet of Things, and providing a valid and low-cost positioning service for the mobile robots in indoor environments has become an important issue. To solve this challenge, we developed a factor graph optimization (FGO) coupled with adaptive unscented Kalman filter (AUKF) method for real-time indoor localization. Firstly, we utilized the AUKF to fuse the inertial measurement unit (IMU) and wheel odometer to obtain the coarse pose estimation. Simultaneously, the IMU’s acceleration and gyroscope zero biases, the mounting angle, and the lever arm length are estimated. The introduction of adaptive factors improves the robustness of the filter. Secondly, we presented the FGO for fusing AUKF information and 2D LiDAR information to enhance the accuracy and robustness of the LiDAR-based pose estimation in the LiDAR-degradation environments. Finally, the FGO-optimized information is utilized to correct the state of the AUKF for further improving the accuracy of the pose estimation. The field experiment results show that the proposed method improved the positioning accuracy by approximately 25.5% and 37.2%, respectively, compared with AUKF and AUKF+PL-ICP. In the LiDAR-degradation environments, the proposed method improved the positioning accuracy by about 85.5%. This confirms the method’s effectiveness for accurate and reliable positioning in indoor environments.

Accurate and robust localization is essential for autonomous driving in complex, dynamic environments and across various motion scenarios. Due to signal blockage, Global Navigation Satellite Systems (GNSS) positioning is not reliable in urban complex environments, and inertial navigation systems (INS) is widely integrated using the Kalman filtering for continuous and robust vehicle localization. On the one hand, INS suffers from accumulated errors and it poses a challenge for precise vehicle localization in complex environments, where GNSS has degraded performance. On the other hand, the Kalman filtering takes certain empirical assumptions regarding the probability models of process noise and measurements, which may be not matched with the real-world conditions. To address these issues, we propose a data and model jointly driven neural-Kalman solution for vehicle inertial localization to enhance positioning accuracy and robustness in various motion states under GNSS-denied environments. The proposed solution exploits the data-driven neural networks to real-time predict the covariance of process noise and three-dimensional (3D) vehicle velocity. Within the neural networks, the proposed loss function dynamically adjusts the error penalty weight and establish a connection between the uncertainty and estimation to improve the localization robustness. Finally, the predicted covariance of process noise and 3D velocity are incorporated with the adaptive Kalman filtering to improve the vehicle localization accuracy. We evaluate the proposed method using two datasets, and the results indicate that the average RMSE of 3D velocity regression is reduced by 56.33% and 50.08%, respectively, compared to two deep learning methods. In the positioning accuracy evaluation experiment, the proposed neural Kalman filter method reduces the average ATE and RTE by 21.10% and 23.96% compared with the state-of-the-art baseline methods in two datasets.

Point cloud registration serves as a fundamental problem across multiple fields including computer vision, computer graphics, and remote sensing. While local feature descriptors (LFDs) have long been established as a cornerstone for point cloud registration and the LFD-based approach has been extensively studied, the field has witnessed significant advancements in recent years. Despite these developments, the research community lacks a systematic review to consolidate these contributions, leaving many researchers unaware of recent progress in LFD-based registration. To address this gap, we present a comprehensive review that critically examines both state-of-the-art and widely referenced methods across all subtasks of LFD-based registration. Our work provides: (1) an extensive survey of existing methodologies, (2) in-depth analysis of their respective strengths and limitations, (3) insightful observations and practical recommendations, and (4) a thorough summary of relevant applications and publicly available datasets. This systematic overview offers valuable guidance for researchers pursuing future investigations in this domain.

Traditional ionospheric models were mostly constructed based on a single layer assumption from Global Navigation Satellite System (GNSS) observations, while it cannot capture vertical information of the ionosphere. This study proposes a new method to construct a double-layer ionospheric model based on constraints from a three-dimensional ionospheric model, whereby the bottom and topside ionospheric TEC can be represented by two spherical harmonic (SH) functions. The new improved model allows two SH functions to capture the spatiotemporal TEC variations across the vertical range of the ionosphere. The determination of the two thin layer heights (TLHs) in the double-layer model is achieved through minimum mapping function error. Moreover, the performance of the new model is validated using GPS, BDS, and Galileo data from the International GNSS Server (IGS) Network, and compared with the global ionospheric map (GIM). During the experiment period, the results indicate that (1) the TLHs of the bottom and topside ionosphere exhibit distinct spatiotemporal trends with the optimal global heights as 350 km and 650 km, respectively; (2) the average relative accuracies of the bottom and topside ionospheric models are up to 86.80 % and 85.33 %, respectively; (3) the new model demonstrates an improvement of approximately 20–27 % in terms of TEC when compared to the GIM model, with the RMS better than 4.64 TECU, 2.99 TECU, and 3.61 TECU in the low, middle, and high latitudes, respectively; and (4) with the increase of geomagnetic activity, the performance of the double-layer model shows a slight decline, but its relative accuracy can still reach over 84.8%.

Indoor navigation and positioning services for pedestrians are challenging because of the lack of satellite signals and the unpredictability of pedestrian motion. The inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) algorithm can provide continuous position estimation for individual pedestrians. However, the accumulation of errors leads to inaccurate pedestrian position results. Radio signals such as ultra-wideband (UWB) can range between pedestrians and anchors and provide high-precision positioning information; nonetheless, radio positioning requires infrastructure deployment and maintenance in indoor environments, thus limiting the popularization and implementation of these technologies. In this paper, a neural network aided factor graph optimization (NN-FGO) method was proposed for collaborative pedestrian navigation (CPN). It integrates IMU and UWB sensors to implement PDR for individual pedestrians and CPN for the Ad-Hoc network, and it is infrastructure-free since all the sensors are wearable. For a small or sparse network, ranging constraints will be insufficient to implement an acceptable CPN. A neural network model was suggested for human activity recognition and position loopback detection, which provide virtual constraints for pedestrians. For the heterogeneous problem caused by multiple collaborative signals and constraints, FGO was employed to solve the motion states of multi-pedestrians and multi-epochs. The real experimental results revealed that NN-FGO can provide 92% accuracy in activity classification. Compared with the extended Kalman filter based CPN, the average position error decreased by 19.6% and 16.0% with triangular and parallel straight geometries, respectively.

Radio fingerprinting positioning is widely used for smartphone location-based services and Internet of Things applications given its high availability and low cost. Radio fingerprinting-based algorithms, however, are subject to the forced matching problem and often yield estimated positions even when a user is actually located outside of the fingerprint region. A positioning solution in multistory buildings should be able to locate positions accurately on the current floor; but these methods may generate unreasonable positioning trajectories, such as irrationally passing through a wall, when fingerprinting positioning is fused the with inertial measurement unit to further improve the accuracy. A radio map must be surveyed dynamically on-the-move, as a dynamic fingerprint, to reduce the time costs and on-site workload. Unlike static fingerprint-based methods, dynamic fingerprinting samples are sparse. Thus, we propose an enhanced indoor positioning solution using spatial context knowledge, extracted from the sparse dynamic fingerprints. In the offline stage of radio map calculation, we extract the dynamic fingerprint features and store them in a spatial features database to reduce the computational time and storage space complexity. The proposed floor detection, region recognition, and path correction algorithms identify the online spatial contexts from the stored spatial features to improve positioning performance. This solution was applied on smartphones combining WiFi and Bluetooth low-energy radio signals in two typical scenarios. The experimental results show that the floor detection accuracy reached 99% while region recognition accuracy reached 90.75%. The positioning path correction method enhances the accuracy of smartphone indoor positioning from 3.27 to 2.56 m.

The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M3VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences. Besides, we also incorporate the normal-depth consistency in the 3D point cloud format to improve the accuracy and continuity of the estimated depth maps. Experimental results show that M3VSNet establishes the state-of-the-arts unsupervised method and achieves better performance than previous supervised MVSNet on the DTU dataset and demonstrates the powerful generalization ability on the Tanks & Temples benchmark with effective improvement.

Wi-Fi-based indoor localization gained a lot of attention over recent years due to low cost and open access properties. However, existing schemes might not be applicable in the real environment if their robustness is low. This paper presents CLRS, a novel distributed Indoor Positioning System (IPS) with high robustness which uses Wi-Fi signals to divide the space twice based on Angle of Arrival (AoA) and Effective Channel State Information (ECSI). The proposed scheme trade the redundancy of Access Point (AP) quantity to improve the tolerance of data measurement error. We performed simulations as well as real-world experiments, in which simulation results proved that the theoretical average error is the least when the routers are placed vertically in our localization method while the real-world experiments proved the high accuracy and robustness of CLRS.

Abstract Interest point detection plays an important role in many computer vision applications. This work is motivated by the light detection and ranging odometry task in autonomous driving. Existing methods are not capable of detecting enough interest points in unstructured scenarios where there are little constructions or trees around, and correspondingly light detection and ranging odometry will fail to continuous localisation. An interest point detector is proposed for detecting interest points from multi‐beam light detection and ranging point cloud using unsupervised convolutional neural network. The point cloud is projected into a two‐dimensional structured data according to the scanning geometry. Then the convolutional neural network filters trained in an unsupervised manner are used to generate a local feature map with the two‐dimensional structured data as input. Finally, interest points are obtained by extracting the grids that have significant differences with their neighbour grids. Based on an odometry benchmark, the experiments show that the proposed interest point detector can capture more local details, which contributes to more than 16% error decrease in point cloud registration in highway scenes.

As a part of Internet-of-Things applications, various smartphone-based indoor location services have considerable commercial value. It is largely agreed that the integration of multiple sensors is the preferable solution for improving the performance of smartphone indoor positioning, thanks to the diversity of built-in sensors. However, heading error remains a challenge for smartphone indoor positioning, especially in complex indoor scenes. This article, therefore, proposes a heading estimation solution to enhance the accuracy and reliability of smartphone indoor positioning. The extended Kalman filter (EKF)-based solution fuses smartphone built-in motion sensors, magnetometers, building map knowledge, and fingerprinting coarse positions from Wi-Fi or Bluetooth. First, the context of pedestrian mobility and scene knowledge is inferred by combining these data. Then, a scene augmentation strategy and magnetic interference online detection method are applied to calibrate the gyro accumulation error and improve the heading estimation accuracy. Additionally, quasistatic and low-dynamic judgments and online calibration are used to mitigate gyro drift. The proposed solution is implemented on a smartphone device and validated in several experiments under natural pedestrian mobility and complex indoor scenarios. Experiments show that the accuracy of heading estimation is improved from 13.1° to 2.0°. The improved heading estimation enhances the accuracy of smartphone indoor positioning from 3.57 to 0.90 m. The proposed solution is applicable to real location-based service scenarios.

Fusions of LiDARs (light detection and ranging) and cameras have been effectively and widely employed in the communities of autonomous vehicles, virtual reality and mobile mapping systems (MMS) for different purposes, such as localization, high definition map or simultaneous location and mapping. However, the extrinsic calibration between a camera and a 3D LiDAR is a fundamental prerequisite to guarantee its performance. Some previous methods are inaccurate, have calibration error that is several times the beam divergence, and often require special calibration objects, thereby limiting their ubiquitous use for calibration. To overcome these shortcomings, we propose a novel and high-accuracy method for the extrinsic calibration between a camera and a 3D LiDAR. Our approach relies on the infrared images from a camera with an infrared filter, and the 2D-3D corresponding points in a scene with the corners of a wall can be extracted to calculate the six extrinsic parameters. Experiments using the Velodyne VLP-16 sensor show that the method can achieve an extrinsic accuracy at the level of the beam divergence, which is fully analyzed and validated from two different aspects. Therefore, the calibration method in this paper is highly accurate, effective and does not require special complicated calibration objects; thus, it meets the requirements of practical applications.

Trusted execution environment (TEE) is a promising technique to protect user programs and data on public cloud environments. To support unmodified applications running, many TEE runtime systems have been proposed. However, a major drawback of the existing schemes is the lack of interface protection. This problem may lead to many security problems, such as memory information leakage and malicious codes attacks. To tackle this problem, we propose SeRoT, a new secure runtime system on trusted execution environments. Our secure runtime system first provides some core functions to the enclave programs. Then we protect the host interface at two levels, binary interface level and application interface level. In these two levels, we prevent the adversary interfacing with malicious messages. Furthermore, we implement SeRoT on a RISC-V based platform and show our scheme is average about 10% faster than Keystone on two popular and representative benchmarks.

With the rapid development of the Internet and combined with outsourcing, a new paradigm - crowdsourcing which shines brilliantly as a new labor mode. However, the existing pricing strategies for crowdsourcing tasks have several undesirable problems, e.g., no universal pricing model, not meeting the multiple requirements of users, pricing rely too much on decision makers, etc., which bring an unreasonable allocation of task rewards so as to make the pricing results subjective and uncontrollable. Therefore, this paper proposes a reverse pricing mechanism based on VCG and quality all-pay for collaborative crowdsourcing (VCG-QCP). The actual crowdsourcing scenario is considered with VCG mechanism, and the concept of quality all-pay is introduced to evaluate the work quality of workers who might perform the task. Then a general reverse pricing model is established by mathematical modeling, and the pricing algorithm is designed based on this model. Simulations show that the proposed method can achieve higher algorithm efficiency, higher task completion quality, a reasonable balance of benefits between employers and workers, and ensuring the truthfulness of workers' bidding.

Software attacks like worm, botnet, and DDoS are the increasingly serious problems in IoT, which had caused large-scale cyber attack and even breakdown of important information infrastructure. Software measurement and attestation are general methods to detect software integrity and their executing states in IoT. However, they cannot resist TOCTOU attack due to their static features and seldom verify correctness of control flow integrity. In this paper, we propose a novel and practical scheme for software trusted execution based on lightweight trust. Our scheme RIPTE combines dynamic measurement and control flow integrity with PUF device binding key. Through encrypting return address of program function by PUF key, RIPTE can protect software integrity at runtime on IoT device, enabling to prevent the code reuse attacks. The results of our prototype’s experiment show that it only increases a small size TCB and has a tiny overhead in IoT devices under the constraint on function calling. In sum, RIPTE is secure and efficient in IoT device protection at runtime.

Motivated by the interference of appendages in airline baggage dimension detection using three‐dimensional (3D) point cloud, a minimum elastic bounding box (MEBB) algorithm for dimension detection of 3D objects is developed. The baggage dimension measurements using traditional bounding box method or shape fitting method can cause large measurements due to the interference of appendages. Starting from the idea of ‘enclosing’, an elastic bounding box model with the deformable surface is established. On the basis of using principal component analysis to obtain the main direction of the bounding box, the elastic rules for deformable surfaces are developed so as to produce a large elastic force when it comes into contact with the main body part and to produce a small elastic force when it comes into contact with the appendages part. The airline baggage measurement shows how to use MEBB for dimension detection, especially for the processing of isotropic density distribution, the elasticity computing and the adaptive adjustment of elasticity. Results on typical baggage samples, comparisons to other methods, and error distribution experiments with different algorithm parameters show that the authors’ method can reliably obtain the size of the main body part of the object under the interference of appendages.

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data.

Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques.

Microphone arrays, also known as acoustic antennas, have been extensively used for sound localization. Small-scale microphone arrays have especially been used in teleconferences and game consoles due to their small dimension and easy deployment. In this article, we present an approach to locating a sound source using a small linear microphone array. We describe the fundamentals of linear microphone arrays and analyze the impact of geometry in terms of positioning accuracy using the dilution of precision (DOP) concept. The generalized cross-correlation (GCC) based on the phase transform (PHAT) weighting function is used to estimate the time difference of arrivals in a microphone array. Given the time differences, we use both closed-form and iterative optimization solutions to calculate the coordinates of the sound source. In order to evaluate the performances of the solutions applied in this paper, simulations and field tests were conducted. Simulation results show that the closed-form algorithm gives a positioning error of less than 5 cm in a 10-by-10 meter room when the geometry of a microphone array is good and the signal to noise ratio (SNR) is high. Linear small microphone arrays have lower performances compared to a non-linear distributed array. When the scale of a linear array is reduced, the positioning accuracy decreases dramatically. With a small linear array, the iterative optimization algorithm gives much better performance compared to the closed-form algorithm. Field tests were conducted in an 11-by-5.6 meter room using a linear array with a length of 0.23 meters. Positioning results show an average error of 0.25 meters along the axis parallel to the linear array and 0.53 meters error along the axis which is perpendicular to the linear array.