Real-time Position Research Articles

A 3D Model-Based Framework for Real-Time Emergency Evacuation Using GIS and IoT Devices

Advancements in 3D modelling technology have facilitated more immersive and efficient solutions in spatial planning and user-centred design. In healthcare systems, 3D modelling is beneficial in various applications, such as emergency evacuation, pathfinding, and localization. These models support the fast and efficient planning of evacuation routes, ensuring the safety of patients, staff, and visitors, and guiding them in cases of emergency. To improve urban modelling and planning, 3D representation and analysis are used. Considering the advantages of 3D modelling, this study proposes a framework for 3D indoor navigation and employs a multiphase methodology to enhance spatial planning and user experience. Our approach combines state-of-the art GIS technology with a 3D hybrid model. The proposed framework incorporates federated learning (FL) along with edge computing and Internet of Things (IoT) devices to achieve accurate floor-level localization and navigation. In the first phase of the methodology, Quantum Geographic Information System (QGIS) software was used to create a 3D model of the building’s architectural details, which are required for efficient indoor navigation during emergency evacuations in healthcare systems. In the second phase, the 3D model and an FL-based recurrent neural network (RNN) technique were utilized to achieve real-time indoor positioning. This method resulted in highly precise outcomes, attaining an accuracy rate over 99% at distances of no less than 10 metres. Continuous monitoring and effective pathfinding ensure that users can navigate safely and effectively during emergencies. IoT devices were connected with the building’s navigation software in Phase 3. As per the performed analysis, it was observed that the proposed framework provided 98.7% routing accuracy between different locations during emergency situations. By improving safety, building accessibility, and energy efficiency, this research addresses the health and environmental impacts of modern technologies.

Robot Tracking Navigation Based on Visual SLAM

Visual Simulation Localization and Mapping technology is an important research direction in the fields of computer vision and robotics. The research background of this technology mainly stems from the insufficient accuracy of traditional positioning and map construction methods in the face of environmental changes. With the development of artificial intelligence, this technology has been involved in multiple disciplinary fields. Since 2015, researchers have begun to focus on the combination with deep learning to improve algorithm robustness, dynamic scene planning and other aspects. This article which introduces the existing tracking car system and visual SLAM (Simultaneous Localization and Mapping) system framework explores the robot tracking and navigation technology based on visual SLAM. Visual SLAM technology provides robots with an autonomous navigation solution that does not require external sensors through feature extraction, real-time positioning, mapping, and closed-loop detection. This article also discusses the advantages and disadvantages of visual SLAM navigation, and offers insights into the future development of VSLAM technology in robot tracking navigation.

Using LSTM with Trajectory Point Correlation and Temporal Pattern Attention for Ship Trajectory Prediction

Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions.

Real-time position path smoothing for robotic manipulators by constructing Composite Trajectory Spline

Aiming at smoothing discontinuous corners in robotic tool paths, a novel spline named “Composite Trajectory Spline”(CT-Spline) is proposed in this paper. Based on CT-Spline, the real-time position path smoothing for robotic manipulators is realized. CT-Spline enables analytical trajectory planning and interpolation without curve length computation, significantly enhancing trajectory generation efficiency and avoiding numerical errors. By directly controlling resultant trajectory acceleration, CT-Spline avoids decomposing it into tangential and normal accelerations, ensuring adherence to the maximum acceleration constraint while fully utilizing acceleration potential. The geometric shape of CT-Spline is determined by three control points and a trajectory model. This paper further develops C1 and C3 continuous CT-Splines based on two different trajectory models, combined with a velocity look-ahead algorithm to achieve real-time local smoothing of path corners. Additionally, an adaptive control algorithm of smoothing error is introduced to dynamically maximize trajectory velocity. Simulations and experiments validate the effectiveness of CT-Spline, and demonstrate that the proposed C3 continuous trajectory smoothing method has several advantages over the method based on Pythagorean-hodograph splines in terms of kinematic constraints, velocity, smoothing errors, and real-time performance.

Multi-Global Navigation Satellite System (GNSS) real-time tropospheric delay retrieval based on state-space representation (SSR) products from different analysis centers

Abstract. The troposphere plays an important role in a range of weather and various climate changes. With the development of the Global Navigation Satellite System (GNSS), the zenith tropospheric delay (ZTD) retrieval using GNSS technology has become a popular method. Research on ZTD accuracies of state-space representation (SSR) corrections from different analysis centers derived from real-time precise point positioning (RT-PPP) is important for Earth observation correction, meteorological disaster forecasting, and warning with the increasing abundance of state-space representation (SSR) products obtained by the International GNSS Service (IGS) analysis center. Therefore, accuracies and availability of real-time orbits and clock errors obtained by the Chinese Academy of Sciences (CAS), GMV Aerospace and Defense (GMV), Centre National d'Etudes Spatiales (CNE), and Wuhan University (WHU) are evaluated, and the RT positioning performance and ZTD accuracies are analyzed for Global Positioning System (GPS), Galileo (GAL), and BeiDou Navigation Satellite System-3 (BDS3) satellites. The results indicate that CAS has the higher satellite availability, providing SSR corrections for 82 GPS, Galileo, and BDS3 satellites. The accuracies of GPS, Galileo, and BDS3 orbits are best at WHU, CAS, and WHU with values of 5.57, 5.91, and 11.77 cm, respectively; the standard deviations (SDs) of clock error are all better than 0.22, 0.19, and 0.55 ns, and the root mean square errors (RMSEs) are better than 0.54, 0.32, and 1.46 ns. CAS has the best signal-in-space ranging errors (SISREs) followed by WHU, while CNE and GMV are worse. In the RT-PPP test, convergence times for CAS and WHU are 14.9 and 14.4 min, respectively, with 3D positioning accuracy for both of around 3.3 cm, which is better than for CNE and GMV. Among them, WHU SSR has the higher accuracy of RT-PPP-derived ZTD, with an RMSE of 6.06 mm and desirable availability with a completeness rate of 89 %.

Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification

Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications.

Imaging error reduction in radial cine-MRI with deep learning-based intra-frame motion compensation

Objective.Radial cine-MRI allows for sliding window reconstruction at nearly arbitrary frame rate, promising high-speed imaging for intra-fractional motion monitoring in magnetic resonance guided radiotherapy. However, motion within the reconstruction window may determine the location of the reconstructed target to deviate from the true real-time position (target positioning errors), particularly in cases of fast breathing or for anatomical structures affected by the heartbeat. In this work, we present a proof-of-concept study aiming to enhance radial cine-MR imaging by implementing deep-learning-based intra-frame motion compensation techniques.Approach.A novel network (TransSin-UNet) was proposed to continuously estimate the final-position image of the target, corresponding to end of the frame acquisition. Within the radial k-space reconstruction window, the spatial-temporal dependencies among the sinogram representation of the spokes were modeled by a transformer encoder subnetwork, followed by a UNet subnetwork operating in the spatial domain for pixel-level fine-tuning. By simulating motion-dependent radial sampling with (tiny) golden angles, we generated datasets from 25 4D digital anthropomorphic lung cancer phantoms. The network was then trained and extensively evaluated across datasets characterized by varying azimuthal radial profile increments.Main Results.The method required additional 4.8 ms per frame over the conventional approach involving direct image reconstruction with motion-corrupted spokes. TransSin-UNet outperformed architectures relying solely on transformer encoders or UNets across all the comparative evaluations, leading to a noticeable enhancement in image quality and target positioning accuracy. The normalized root mean-squared error decreased by 50% from the initial value of 0.188 on average, whereas the mean Dice similarity coefficient of the gross tumor volume increased from 85.1% to 96.2% in the investigated cases. Furthermore, the final-positions of anatomical structures undergoing substantial intra-frame deformations were precisely derived.Significance.The proposed approach enables an effective intra-frame motion compensation, offering an opportunity to reduce errors in radial cine-MR imaging for real-time motion management.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results.

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Real-time fuzzy position control and design of Star parallel robot

Real-time fuzzy position control and design of Star parallel robot

Parallel Force Feedback Device Design With Constant Jacobian Matrix and Net Weight Fully Balanced

Abstract Force feedback devices offer realistic and intuitive feedback to operators, and achieving higher accuracy and transparency has been a prominent research focus in this field. However, errors in real-time force or position calculations are inevitable, which can have adverse effects on the accuracy of the equipment. This article presents the design of a parallel mechanism that incorporates a constant force and velocity mapping relationship based on the type synthesis of the constant Jacobian matrix. Specifically, the mechanism maintains a fixed direction of force applied to the operating platform by the drive pair, which is mounted on the fixed platform in each limb, irrespective of position changes. This unique feature enables the mechanism to achieve a complete net weight balance. The implementation method is detailed in this article, which involves adding appropriate counterweights to achieve a balance between the weight of the connecting rod and the overall gravity of the operating platform. Furthermore, the optimization of structural parameters helps to improve the performance of the developed prototype. The proposed design scheme not only addresses the fundamental reduction of errors caused by real-time force and position mapping solving but also enhances operational transparency. Finally, simulation and experimental tests are conducted to validate the proposed theory.

Landslide deformation velocity real-time monitoring based on time-differenced carrier phase and its fault detection method

GNSS technologies such as Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) have become indispensable for landslide monitoring. However, RTK relies on stable reference stations and robust communication network, rendering it unusable without reference station data or communication technology support. Real-time PPP technology provides decimeter level monitoring accuracy and suffers from long convergence times, failing to meet the real-time and reliable monitoring requirements during landslide destabilization phase. This paper presents the first study to investigate the application of time-differenced carrier phase (TDCP) technology for real-time monitoring of landslide deformation velocity. An adaptive fault detection method is proposed, which combines prior closure error outlier detection with the chi-squared residual-based fault detection and exclusion. Unlike traditional methods that require iteratively recalculating solutions with each satellite exclusion, our approach significantly reduces computational time. The effectiveness of the proposed method has been validated through two sets of real measurement data. The results demonstrate that TDCP technology enables real-time monitoring of deformation velocity at the millimeter level, and the proposed adaptive fault detection method effectively identifies measurement anomalies in complex environments. In static complex environments, the standard deviations of the GPS/BDS/GLONASS/Galileo combined solution in the E, N, and U directions are 1.7 mm/s, 1.7 mm/s, and 3.9 mm/s, respectively. During rapid sliding in open environments, the root mean square of the differences between TDCP and RTK monitoring results are 2.2 mm/s, 3.3 mm/s, and 4.6 mm/s, respectively.

Inovasi Parkir Cerdas: Prediksi Posisi Kendaraan dengan Teknologi RFID dan Arduino Mega 2560 R3

Urban parking management is an urgent challenge in an ever-increasing vehicle mobility era. This research develops an intelligent parking system that integrates RFID (Radio-Frequency Identification) technology and Arduino Mega 2560 R3 to predict the vehicle's position in real time. The initial literature study identified various approaches to parking management, but none comprehensively combined RFID and Arduino Mega 2560 R3. Therefore, this study fills this gap. The research method involves literature study, system design, hardware and software development, and field testing. In the proposed system, vehicles are equipped with RFID cards, and the parking infrastructure is equipped with RFID readers. When the vehicle enters the parking area, the system reads the RFID card and predicts available parking positions. The results of field tests show an increase in the efficiency of parking space use of around 37% to 46%, and the accuracy of parking position prediction is above 90%. This research provides effective solutions for urban parking management, reducing traffic congestion and air pollution. This system also increases the driver's comfort in finding a suitable parking space. Thus, this research has the potential to support more sustainable urban development by optimizing the use of parking lots and reducing the negative impact of motorized vehicles in urban areas.

Affordable Real-Time PPP—Combining Low-Cost GNSS Receivers with Galileo HAS Corrections in Static, Pseudo-Kinematic, and UAV Experiments

The Galileo High Accuracy Service (HAS) is a free of charge Global Navigation Satellite System (GNSS) augmentation service provided by the European Union. It is designed to enable real-time Precise Point Positioning (PPP) with a target accuracy (at the 95% confidence level) of 20 cm and 40 cm in the horizontal and vertical components, respectively, to be achieved within 300 s. The performance of the service has been confirmed with geodetic-grade receivers. However, mass market applications require low-cost GNSS receivers connected to low-cost antennae. This paper focuses on the performance of the real-time static and kinematic positioning achieved with Galileo HAS and low-cost GNSS receivers. The study is limited to GPS + Galileo dual-frequency positioning, thus exploiting the full potential of Galileo HAS SL1. We demonstrate that the target accuracy of Galileo HAS SL1 is reached with both geodetic-grade and low-cost receivers in dual-frequency static and kinematic applications in open-sky conditions. Precision of a few centimeters is reached for static positioning, while kinematic positioning results in subdecimeter precision. Vertical accuracy is limited by missing phase center offset models for low-cost antennas. In general, the performance of low-cost hardware using Galileo HAS for real-time PPP is comparable to that of geodetic-grade hardware. Therefore, combining low-cost GNSS receivers with Galileo HAS is feasible and justified.

Application and Prospect of Strapdown Inertial Navigation System in Coal Mining Equipment.

In recent years, with the rapid construction of a safe, clean, efficient and sustainable modern energy system, the intelligence of coal mining has become the inevitable direction of the development of the coal mining industry. The intelligence of coal mining system and equipment is the core of intelligent mining, and the positioning technology of mining equipment is the key to underground intelligent mining. The strapdown inertial navigation system can make up for the shortcomings of traditional GPS positioning systems and laser positioning because of its strong anti-interference, high precision and real-time monitoring, and ability to carry out real-time dynamic positioning of mining equipment in underground coal mines. This paper briefly introduces the basic principle of the strapdown inertial navigation system, analyzes the application of the existing strapdown inertial navigation system in mining equipment, and lists and analyzes the research status of the improvement strategy of the inertial navigation system, such as zero speed correction and integrated navigation technology. Finally, by analyzing the application and future development trend of inertial navigation systems in mining equipment, this paper provides more data and method support for the positioning of mining equipment in the future.

Use of a Microelectromechanical Systems Sensor for Objective Measurements of Abnormal Head Posture in Congenital Superior Oblique Palsy Patients.

The purpose of this study was to design an objective method for measurement of head positions as achieved with use of a microelectromechanical systems (MEMS) sensor. In addition, to use this system to observe the abnormal head position (AHP) in patients with congenital superior oblique palsy (SOP) before and after their surgery. An MEMS sensor was designed for recording of the pitch, roll, and yaw values of the head position in real time. The MEMS sensor was then fixed on the synoptophore from -30 degrees to +30 degrees positions horizontally and vertically to test the accuracy of these measurements. Then, we tested 13 participants with AHP using the MEMS method and the photographic method and compared their correlations. Finally, the pitch, roll, and yaw values of head positions were measured using this MEMS sensor in 31 patients with congenital SOP as performed before and after their surgery. The MEMS sensor (LPMS-B2; Alubi, Guangzhou, China; 400 hertz [Hz]), as based on the theory of a gyroscope, was designed and connected to a smartphone via Bluetooth. It was able to conveniently record the patient's pitch, roll, and yaw head positions in real time, recordings which were consistent with the scales of the synoptophore (P > 0.05) and good correlations with the photographic method (P < 0.001). The main preoperative AHP in patients with SOP was roll (22/31, 71%). Pre- and postoperative vertical deviations were 16.4 ± 7.3 prism diopters (PD) and 4.1 ± 4.2 PD, respectively (P = 0.001). The AHP in patients with SOP was positively correlated with the angle of extorsion in the dominant eye (P = 0.01), rather than that of the vertical deviation. The MEMS sensor described in this report is a simple, practical, and accurate objective device for use in head position measurements. In patients with SOP, the AHP is related to the angle of extorsion in the dominant eye. The MEMS sensor was designed as a micro-wireless dynamic high-precision device for AHP measurement, which has the potential for use in a clinic.

An Automatic Mapping Method of Navigation Map for Outdoor Road Scenes

Abstract. In the process of autonomous navigation of outdoor mobile robots, four modules are involved: perception, localization, planning, and controlling. The perception module utilizes sensors such as cameras and radars based on various principles to analyze the robot's surroundings in real-time. The localization module uses GPS (Global Positioning System), IMU (Inertial Measurement Unit), and prior maps for real-time positioning analysis. The planning module plans optimal path based on the outputs of the first two modules, and the controlling module directs the robot's chassis to move along the planned optimal path. For the localization module, the accuracy of GPS positioning results heavily depends on weather conditions and GPS signal receptions. Even if the positioning results of imu are integrated, the positioning accuracy still cannot meet the needs of robot navigation. Therefore, using prior maps for repositioning can compensate for this accuracy deficiency. The planning module also requires path planning based on prior maps. If the a priori map storage is large, it will lead to difficulties in usage, maintenance, and updates. Therefore, it is crucial to research lightweight navigation map mapping methods. In this paper, an automatically mapping method of lightweight navigation maps is proposed, combining cameras and LiDAR (Light Detection and Ranging), including semantic informations necessary for outdoor navigation positioning, such as pole-like objects and traffic signs for robot longitudinal positioning, and lane line elements for robot lateral positioning. This method automatic generates robot navigation maps in Lanelet2 format, providing support for subsequent positioning and path planning modules.

Lightweight Indoor Positioning System Based on Multiple Self-Learning Features and Key Frame Classification

Abstract. Traditional indoor positioning technologies mostly require advanced installation of hardware devices, resulting in high costs and long-term maintenance. With advancements in image recognition and deep learning technologies, indoor visual positioning based on image recognition has become increasingly mature. This method offers the benefits of low cost and does not require additional hardware installation. However, it still has inherent defects, such as cumbersome data collection, complex algorithms, and universality. To minimize indoor information pre-collection cost, improve versatility, and enable rapid deployment in low-performance mobile devices, this paper proposes a lightweight indoor positioning system based on multiple self-learning features and key frame classification. The system is divided into two stages: preprocessing and real-time positioning. In the preprocessing stage, image information is collected for the entire indoor environment, and a key-frame recognizer is trained based on the image information. Simultaneously, an environmental feature information database is established. In the real-time positioning stage, the system first uses mobile devices such as smartphones to obtain real-time video streams. A key frame recognizer based on convolutional neural networks identifies key frames in each video stream frame, thereby obtaining approximate positions for rough positioning. Second, feature points are identified in each frame of the video stream and matched with feature points with location information in the built environmental feature information database to calculate precise positions for fine positioning. It has significant optimizations compared with conventional visual solutions in terms of preprocessing data collection, algorithm performance consumption, and versatility.

Enhancing BDS-3 PPP-B2b real-time positioning performance by tightly integrating MEMS IMU and LiDAR in GNSS-Degraded environment

Currently, BDS-3 can provide satellite-based real-time precise point positioning (PPP) service via its PPP-B2b signal. However, in a complex Global Navigation Satellite System (GNSS)-degraded environment, the BDS-3 PPP-B2b positioning performance is severely influenced. A tightly coupled PPP/microelectromechanical system inertial measurement unit (MEMS IMU)/Light Detection and Ranging (LiDAR) combination is proposed, in which the original pseudorange and carrier observations, IMU measurements, and LiDAR planar features are fused via an extended Multi-State Constraint Kalman Filter (MSCKF). Moreover, the Galileo satellites are further utilized using the broadcast ephemerides to increase the number of available satellites. And to improve the real-time computing efficiency, a planar feature tracking model based on ground segmentation is adopted. The model consists of point cloud segmentation, planar feature extraction and fusion, and data correlation. To evaluate the effectiveness of the proposed method, the experiment is conducted at both a campus and a park. The result shows that approximately sub-meter-level positioning accuracy can be achieved using tightly coupled BDS-3 PPP-B2b, IMU, and LiDAR, while the positioning errors of BDS-3 PPP-B2b and BDS-3 PPP-B2b/IMU are both larger than 5 m. Furthermore, the MEMS IMU and LiDAR measurements show the capability of bridging the gaps in the GNSS data, which enables continuous and stable positioning. Moreover, compared to the state-of-the-art A-LOAM, LEGO-LOAM, and LIO-SAM frameworks, absolute trajectory errors of less than 1.0 and 2.5 m are achieved in the campus and park, respectively, using the proposed algorithm, with improvements of more than 3.40 and 1.58 times.

Under-Canopy Drone 3D Surveys for Wild Fruit Hotspot Mapping

Advances in mobile robotics and AI have significantly expanded their application across various domains and challenging conditions. In the past, this has been limited to safe, controlled, and highly structured settings, where simplifying assumptions and conditions allowed for the effective resolution of perception-based tasks. Today, however, robotics and AI are moving into the wild, where human–robot collaboration and robust operation are essential. One of the most demanding scenarios involves deploying autonomous drones in GNSS-denied environments, such as dense forests. Despite the challenges, the potential to exploit natural resources in these settings underscores the importance of developing technologies that can operate in such conditions. In this study, we present a methodology that addresses the unique challenges of natural forest environments by integrating positioning methods, leveraging cameras, LiDARs, GNSS, and vision AI with drone technology for under-canopy wild berry mapping. To ensure practical utility for fruit harvesters, we generate intuitive heat maps of berry locations and provide users with a mobile app that supports interactive map visualization, real-time positioning, and path planning assistance. Our approach, tested in a Scandinavian forest, refines the identification of high-yield wild fruit locations using V-SLAM, demonstrating the feasibility and effectiveness of autonomous drones in these demanding applications.