Local Technology Research Articles

Indoor Positioning Based Ranging: A Review and Literature Survey of UWB and Li-Fi Technologies

Why IPS (Indoor Positioning Systems)? This issue is one of the most challenging things to solve in wireless localization due to the lack of a Global Positioning System (GPS) and the existence of distinctive radio propagation characteristics. Although there are different localization options available, the accuracy of localization cannot satisfy customers' requirements. Positioning algorithms could be grouped into two groups, including range-based and range-free techniques. Before actually putting a localization technique into practice, the accuracy of those techniques is of the utmost importance. Range-based methods can often attain great accuracy with the help of specialized hardware, and this accuracy can be dependent on either the distances between nodes or the angles between them. In this paper, a comparison of different strategies utilized for positioning is presented, as well as an analysis of the pros and cons associated with supporting technologies for each strategy. A literature survey of the recent IPS technologies range-based with focuses on Ultra-Wide Band (UWB) and Light Fidelity (Li-Fi) is presented with significant recommendations. This literature considered the accuracy, complexity, scalability, cost, latency, deployment, and usability, as well as strengths, shortcomings, approaches, and issues determined by each work. This paper highlights the most recent research gaps and reviews the most promising findings, with recommendations to the reader and researcher, in UWB and Li-Fi indoor positioning systems over the last five years based on range-based techniques. In addition, this paper serves as a guide that discusses all of the measures that may be utilized in the process of evaluating localization technologies, and it could be considered a roadmap for existing and new researchers to identify and characterize suitable technologies for creating innovative systems and apps via stand-alone range-based positioning.

Using Sundanese Musical Instrument and Audacity Software to Learn About Sound Waves

Integrating local culture into classes can build meaningful experiences for the students. Suling is a musical instrument originating from West Java’s dominant Sundanese culture and can be an alternative learning medium for teaching sound wave subjects. Using technology can facilitate understanding concepts for students. The Audacity software can help students analyze the spectrum and frequency of the sound produced by Suling. This study aimed to investigate the measurement of the frequency and spectrum of Suling with four and six holes and to analyze the potential for integrating local culture and technology in science learning. The type of research was descriptive to provide an overview of a symptom, event, and actual situation. We used a microphone and Audacity on the computer and each musical instrument in Suling. The study’s results showed that spectrum analysis has a similar pattern in Suling four and six holes and almost had similar frequencies. Local culture has the potential to be implemented in science learning, and technology facilitates students’ understanding of concepts. Suling and Audacity can be used as a learning media for a junior high school practicum on a sound wave subject. Keywords: Sundanese Musical, Instrument and Audacity Software, Sound Waves

Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage

Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.

A comprehensive overview of core modules in visual SLAM framework

Visual Simultaneous Localization and Mapping (VSLAM) technology has become a key technology in autonomous driving and robot navigation. Relying on camera sensors, VSLAM can provide a richer and more precise perception means, and its advancement has accelerated in recent years. However, current review studies are often limited to in-depth analysis of a specific module and lack a comprehensive review of the entire VSLAM framework. The VSLAM system consists of five core components: (1) The camera sensor module is responsible for capturing visual information about the surrounding environment. (2) The front-end module uses image data to roughly estimate the camera’s position and orientation. (3) The back-end module optimizes and processes the pose information estimated by the front-end. (4) The loop detection module is used to correct accumulated errors in the system. (5) The mapping module is responsible for generating environmental maps. This review provides a systematic and comprehensive analysis of the SLAM framework by taking the core components of VSLAM as the entry point. Deep learning brings new development opportunities for VSLAM, but it still needs to solve the problems of data dependence, cost and real-time in practical application. We deeply explore the challenges of combining VSLAM with deep learning and feasible solutions. This review provides a valuable reference for the development of VSLAM. This will help push VSLAM technology to become smarter and more efficient. Thus, it can better meet the needs of future intelligent autonomous systems in multiple fields.

A novel simple laser guidance puncture system for intracerebral hematoma

ObjectiveAccurate localization and real-time guidance technologies for cerebral hematomas are essential for minimally invasive procedures, including minimally invasive hematoma puncture and drainage, as well as neuroendoscopic-assisted hematoma removal. This study aims to evaluate the precision and safety of a self-developed laser-guided device in localizing and guiding hematoma punctures in minimally invasive surgery for intracerebral hemorrhage (ICH). MethodsWe present the components of the device and its operational procedures. Subsequently, surgeons with different titles conduct hematoma puncture experiments using the device on skull models, comparing it to freehand puncture methods and recording the offset distance from the puncture needle tip to the hematoma center. Additionally, we report the application of this device in 10 patients with ICH, assessing its accuracy and safety in comparison with a neuro-navigation system. ResultsIn simulated puncture experiments, the accuracy of the laser-guided group surpasses that of the freehand puncture group, with a significant statistical difference observed between the two groups (P < 0.05). In the laser-guided group, there is no statistically significant difference in puncture accuracy among the surgeons (P > 0.05). In clinical experiments, no relevant surgical complications were observed. The offset distance for the laser-guided group was 0.61 ± 0.18 cm, while the neuro-navigation group was 0.48 ± 0.13 cm. There was no statistically significant difference between the two groups in terms of offset distance (P > 0.05). However, there was a significant difference in surgical duration (P < 0.05), with the former being 35.0 ± 10.5 minutes and the latter being 63.8 ± 10.5 minutes. ConclusionThe current study describes satisfactory results from both simulated experiments and clinical applications, achieved through the use of a novel laser-guided hematoma puncture device. Furthermore, owing to its portability, affordability, and simplicity, it holds significant importance in advancing surgical interventions for ICH, especially in underdeveloped regions.

Study on advanced partial least squares for quality-related fault detection

The issue of quality-related fault detection in the industrial process has attracted much attention in recent years. The partial least squares (PLS) is considered an efficient tool for predicting and monitoring. The modified partial least squares (MPLS) is an extended algorithm for solving the oblique decomposition of PLS, however, the study indicated that the loss of quality variable information may affect the prediction of quality information in the decomposition process of the MPLS algorithm. Furthermore, the detection rate of traditional statistics and static control limit is low, and the existing dynamic control limit has certain limitations. Therefore, a new PLS space-decomposition algorithm called advanced partial least squares (APLS) is proposed. APLS avoids the loss of quality information by orthogonal decomposition of process variables according to their relationship with quality. APLS has a more accurate prediction of quality when process variables contain more noise; the fault false alarm rates (FAR) of quality-related faults are reduced by using the new statistics and thresholds combined with local information increment technology in the process variable principal component subspace. Finally, the effectiveness of the proposed approach is verified by a numerical example and an industrial benchmark problem.

Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing

The identification of branches and bud points is the key to intelligent pruning of dormant grapevine branches and precise positioning of the pruning point on the branch is an important prerequisite for robotic arm pruning. This study takes Cabernet Sauvignon wine grapes as the experimental object and proposes a depth image-based pruning point localization algorithm based on pruning rules. In order to solve the problem of bud recognition in complex backgrounds, this study adopts a detection method that combines semantic segmentation and target detection. Firstly, the semantic segmentation algorithm PSP-net is used to separate the branches and the main stem from the background and the separated image undergoes two kinds of processing: one is to skeletonize it using the Zhang–Suen thinning algorithm and the other is to identify the buds and obtain the center coordinates of the buds using the target-detection method YOLOv5; finally, combining with the depth information of the depth image, we use the coordinates of the buds to determine the location of the pruning point located on the skeleton image. The results show that PSP-net has better results in segmentation performance with mIoU reaching 83.73%. YOLOv5 performs better in target detection with mAP reaching 81.06% and F1 reaching 0.80. The accuracy of this method in determining the location of pruning points reaches 82.35%. It can provide a method for fruit tree pruning robots to determine the location of pruning points.

Room-scale Location Trace Tracking via Continuous Acoustic Waves

The increasing prevalence of smart devices spurs the development of emerging indoor localization technologies for supporting diverse personalized applications at home. Given marked drawbacks of popular chirp signal-based approaches, we aim at developing a novel device-free localization system via the continuous wave of the inaudible frequency. To achieve this goal, solutions are developed for fine-grained analyses, able to precisely locate moving human traces in the room-scale environment. In particular, a smart speaker is controlled to emit continuous waves at inaudible 20kHz , with a co-located microphone array to record their Doppler reflections for localization. We first develop solutions to remove potential noises and then propose a novel idea by slicing signals into a set of narrowband signals, each of which is likely to include at most one body segment’s reflection. Different from previous studies, which take original signals themselves as the baseband, our solutions employ the Doppler frequency of a narrowband signal to estimate the velocity first and apply it to get the accurate baseband frequency, which permits a precise phase measurement after I-Q (i.e., in-phase and quadrature) decomposition. A signal model is then developed, able to formulate the phase with body segment’s velocity, range, and angle. We next develop novel solutions to estimate the motion state in each narrowband signal, cluster the motion states for different body segments corresponding to the same person, and locate the moving traces while mitigating multi-path effects. Our system is implemented with commodity devices in room environments for performance evaluation. The experimental results exhibit that our system can conduct effective localization for up to three persons in a room, with the average errors of 7.49 cm for a single person, with 24.06 cm for two persons, with 51.15 cm for three persons.

Interaction Effect of Export Trade, Foreign Direct Investment and Technological Independent Innovation in China

China has made significant progress in the field of clean energy and sustainable development, with its photovoltaic industry technology leading globally. What has been the trajectory of China’s successful technological catch-up over the past two decades? Is China’s experience sustainable? To clarify the interaction logic between export trade (ET), foreign direct investment (FDI), and technological independent innovation (TII) in China, this paper uses panel data from 31 provinces and cities in the Chinese Mainland between 2000 and 2022. A panel vector autoregression (PVAR) model is constructed from a dynamic endogenous perspective to verify the interaction and regional heterogeneity among the three. The results are as follows: (1) The unified analytical framework shows a significant bidirectional “positive feedback effect” between ET and TII. However, FDI inhibits TII to a certain extent. Furthermore, the correlation between ET and FDI is weak. (2) The impact of ET on TII is most pronounced in the western region, while the central region sees the highest contribution rate of FDI to TII. The self-evolution effect of TII is most evident in the eastern region. This study provides suggestions for the government to develop an adaptation policy for local industrial technology conditions and establish a National Sustainable Systems of Innovation (NSSI) with multiple comparative advantages, and serves as a reference for establishing a “Chinese model”.

Let’s be Frank

A unique bridle bit found at Lochlea crannog, South Ayrshire appears to show a fusion of traditional local Iron Age technology with Continental early medieval styling, specifically the influence of Frankish horsemanship (hereafter equitation) and equipment (hereafter lorinery and tack). The re-examination of the Lochlea bit allows consideration of Scotland’s connections to early medieval Francia as well as highlighting the archaeological potential of the artefacts found at Lochlea, with emphasis on a modern examination of the chronology of the site.

Driving the electric vehicle agenda in Nigeria: The challenges, prospects and opportunities

In Nigeria, a developing country and prominent oil producer, the transition towards electric vehicle adoption is unfolding amidst unique challenges. This study addresses crucial research gaps concerning Electric vehicle adoption in developing nations, with Nigeria as a focal point. Through interviews with 31 experts, we have uncovered a landscape filled with challenges and opportunities. The hurdles include a scarcity of charging infrastructure, a heavy reliance on fossil fuels, affordability issues, and unequal access to energy. On the bright side, there are prospects for cost savings backed by government support, adaptable manufacturers, and the potential for renewable energy utilisation. We propose comprehensive awareness campaigns to fast-track electric vehicle adoption, expanding charging infrastructure, government-driven policies, and integrating localised technology, specifically focusing on last-mile transport. Our study contributes valuable insights into Electric vehicle adoption in developing countries, offering theoretical and practical implications for sustainable transportation solutions.

An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites

Construction robots have been increasingly adopted in construction projects to improve productivity, reduce risk, and speed up work cycles. Visual Simultaneous Localization and Mapping (V-SLAM) technology is widely used in construction robots due to its lightweight weight, cost-effectiveness, and provision of semantic information. On real construction sites, lighting conditions are often challenging due to factors such as uneven lighting intensity, inadequate exposure, or robots in backlit positions (e.g. roughcast houses without artificial lighting, underground car parks), which cause images captured under these conditions to exhibit low signal-to-noise ratio, uneven lighting, color distortion, noise, and other problems. These challenges make the extraction and matching of feature points in V-SLAM difficult, resulting in significant positioning errors or the inability to generate positioning trajectories for construction robotics. Although existing methods for enhancing low-light images can certainly improve image brightness, they still cannot effectively address the localization challenges brought about by low-light construction scenes with uneven illumination and noise. To address the interference of uneven and changing lighting conditions on V-SLAM localization in construction sites, this paper proposes the Unsupervised Reflectance Retinex and Noise model (URRN-Net) to enhance low-light construction images. By using a CNN model based on the Retinex theory to decompose the illumination characteristics of images, we achieve effective brightness restoration of uneven low-light images by utilizing unsupervised loss functions. URRE-Net can reduce the root mean square localization error by >65% compared to low-light images in the ORB-SLAM3 method, and the maximum error is reduced by >71% in on-site experiments. The proposed URRE-Net can be integrated with existing V-SLAM algorithms to provide more robust localization services for low-light construction site applications such as building operations (e.g., interior wall spraying robotic) or construction management tasks (e.g., automatic tunnel inspection).

Smart City Projects Financing

The implementation of advanced digital technologies and innovations in smart cities for the provision of more efficient, sustainable, and inclusive city services, the development of infrastructure, and the improvement of citizens’ quality of life require appropriate financing technologies. In addition to purely public (for example, green bonds, social impact bonds, state subsidies, etc.) and purely private (venture investments, leasing, etc.) financial instruments, various options for public-private partnerships and financing with and against the participation of the community (participatory ), which increase the efficiency of budget financing due to the agreed distribution of risks and responsibilities between stakeholders. The bibliometric analysis of publications indexed by Scopus using the keywords “smart city” and “public-private partnership” using the VOSviwer tool allowed establishing the periods of growth (2013-2019 and 2022-2023) and decline (2019-2022) of the popularity of this topic among scientists, countries-leaders of research activity (USA, India, China, Italy, Spain, Great Britain, Canada and Germany), dominant directions of cross-sectoral research. The analysis of the Smart City Index Report (IMD Smart City Index Report) showed that in 2023, the top 5 leaders were Switzerland (Zurich), Norway (Oslo), Australia (Canberra), Denmark (Copenhagen), and Great Britain (London). With the help of the Google Trends toolkit, an analysis of the dynamics of Internet requests over the past 10 years from citizens of these countries, as well as Ukraine and the world, was carried out using identical keywords. The analysis proved that both in these countries and in the world as a whole, public interest in the development of the smart grid was recorded (in Ukraine, the highest peak (100 GT Scale) was in 2019; in Great Britain and Switzerland – in 2018, in Denmark – in 2017 and 2022, in Australia and Norway – in 2016) against the background of an almost absolute (except for Great Britain and Australia) lack of public interest in issues of public-private partnership. A regression model was developed to study the impact of participatory financing on the readiness level of Ukraine’s regions (as of 2020) to implement smart local development technologies. An integral indicator was used as the resulting variable, within which data on the assessment of the availability of electronic public services and the level of automation and digitization of public services (the number of state and local self-government bodies that provided the possibility of using electronic democracy tools), the level of use electronic platforms for communication with state bodies (the number of registered “E-appeals”, published “E-petitions” and reports on “E-consultations”), the availability of the Internet (the number of Internet subscribers and the share of households that have access to Internet services home). In the modeling process, adjustments were made for the gross regional product and the population of the respective region, the weighting factors were determined by the method of principal components, the estimation of the model parameters was carried out by the method of least squares, the calculations were made using the MS Excel spreadsheet and the Statistica application program package.

Noise Analysis for Unbiased Tree Diameter Estimation from Personal Laser Scanning Data

Personal laser scanning devices employing Simultaneous Localization and Mapping (SLAM) technology have rightfully gained traction in various applications, including forest mensuration and inventories. This study focuses the inherent stochastic noise in SLAM data. An analysis of noise distribution is performed in GeoSLAM ZEB Horizon for point clouds of trees of two species, Norway spruce and European beech, to mitigate bias in diameter estimates. The method involved evaluating residuals of individual 3D points concerning the real tree surface model based on TLS data. The results show that the noise is not symmetrical regarding the real surface, showing significant negative difference, and moreover, the difference from zero mean significantly differs between species, with an average of −0.40 cm for spruce and −0.44 cm for beech. Furthermore, the residuals show significant dependence on the return distance between the scanner and the target and the incidence angle. An experimental comparison of RANSAC circle fitting outcomes under various configurations showed unbiased diameter estimates with extending the inlier tolerance to 5 cm with 2.5 cm asymmetry. By showing the nonvalidity of the assumption of zero mean in diameter estimation methods, the results contribute to fill a gap in the methodology of data processing with the widely utilized instrument.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Autonomous navigation of orchard spraying robots can effectively improve operational efficiency and reduce workload. The conventional robot navigation solutions based on the Global Navigation Satellite System (GNSS) are susceptible to signal loss in orchards due to interference from the canopy of fruit trees. In this paper, an autonomous navigation scheme for orchard spraying robots based on multi-sensor devices and the three-dimensional (3D) LiDAR Simultaneous Localization and Mapping (SLAM) technology. The initial step of the scheme involved the construction of a 3D point cloud map of the orchard using the 3D LiDAR SLAM algorithm. Subsequently, a designed point cloud map transformation algorithm was used to convert the 3D point cloud map of the orchard into a two-dimensional (2D) grid map containing key feature information of the orchard. To enhance the mapping accuracy of the orchard point cloud, a point cloud registration algorithm combining Iterative Closet Point (ICP) and Normal Distributions Transform (NDT) was proposed. This algorithm initially utilized the NDT algorithm for coarse point cloud registration to obtain transformation parameters. Then, the ICP algorithm was applied in conjunction with the obtained parameters for fine registration. Regarding obstacle avoidance, a cooperative obstacle avoidance algorithm based on the Robot Operating System (ROS) multithreaded communication mechanism was introduced. Experimental trials conducted in a standardized spindle-shaped peach orchard validated the successful completion of the SLAM mapping trajectory, which was achieved through the utilization of the proposed NDT_ICP point cloud registration algorithm. The average absolute pose error between the mapped trajectory and the reference trajectory was 1.173 m, with a standard deviation of 0.498 m. The robot's positioning and navigation errors increased with higher speeds, with the largest positioning deviation observed at 1.2 m/s speed, with a maximum lateral positioning error of 7.04 cm. The robot's average lateral navigation error did not exceed 16 cm, and the average heading deviation was less than 8° at different speeds of 0.4, 0.8 and 1.2 m/s. Both the positioning and navigation accuracies of the robot were sufficient to meet the autonomous operational requirements of orchard spraying robots.

Research on Visual Localization and Gripping Technology of Robotic Arm Based on Deep Learning

With the continuous development of industrial automation, robotic arms are more and more widely used in production lines. In order to realize the autonomous positioning and grasping function of robotic arm, this paper studies the visual positioning and grasping technology of robotic arm based on deep learning. The deep learning algorithm processes and analyzes the image, realizes the automatic identification and localization of the target object, and then guides the robotic arm to carry out accurate grasping. The experimental results show that this technology can effectively improve the positioning accuracy and grasping success rate of the robotic arm, which provides strong support for the intelligent upgrade of industrial automation production lines.

Pineapple cultivation under tree canopies of ancestral agroforests in Mexico

Mexico’s contemporary cultivation of pineapple, the king of tropical fruits, depends on intensive monoculture which negatively impacts the environment and human health. This represents a regression in history, as pineapple was traditionally cultivated in agroforests, which preserve the structural characteristics and ecological processes and functions of natural ecosystems. Some of these agroforestry systems remain, having been conserved for centuries by Indigenous and mestizo communities, particularly in the states of Jalisco and Nayarit, showcasing the sustainability and resilience of agroforestry. Unlike modern monoculture, pineapple agroforests incorporate diverse plant species to improve biodiversity and reduce disease, integrating pineapple with various woody species native to the subdeciduous tropical forest. They also provide shade and preserve forest cover, and limit the use of machinery, maximising the use of traditional knowledge and local technologies. The resulting agroecosystems not only sustain pineapple production but also yield additional products such as fruits, coffee, wood and fodder, contributing to food sovereignty and self-sufficiency. Despite the benefits of pineapple agroforests, they remain largely unknown at national and international levels, highlighting a need for greater recognition and support. This requires more data on the costs of establishing pineapple agroforests, as the costs are currently unclear. To encourage policymakers to promote the conservation and maintenance of pineapple agroforests, the article provides insight into their ecological and cultural significance and emphasises the importance of valuing and preserving ancestral agroforestry practices.

Navigating the future: A comprehensive survey of localization systems in autonomous vehicles

&lt;p&gt;This survey paper provides an in-depth analysis of localization systems for autonomous vehicles (AVs), a cornerstone technology crucial for the safe and efficient operation of AVs. The paper encompasses a comprehensive examination of various localization technologies, their comparative analysis, the challenges they face, and the emerging trends shaping their future. We begin by exploring the principal technologies employed in AV localization: GPS, LiDAR, radar, cameras, and ultrasonic sensors. Each technology is scrutinized to understand its strengths, weaknesses, and applicability in different environmental contexts. GPS offers broad geographical positioning but struggles with precision in dense urban areas. LiDAR provides high-resolution mapping but is hindered by adverse weather conditions. Radar ensures reliability in poor visibility but lacks the finer details provided by optical systems. Cameras offer rich visual data but are dependent on lighting conditions, and ultrasonic sensors, while effective for close-range detection, are limited to low-speed applications. The paper then presents a comparative analysis of these technologies against key performance metrics such as accuracy, reliability, latency, scalability, and cost-effectiveness. This analysis is critical in understanding the suitability of each technology for various driving scenarios, from congested urban streets to open highways and in diverse weather conditions. Challenges in AV localization are multifaceted, encompassing technological, environmental, and system integration issues. Technological challenges include sensor limitations and computational constraints, while environmental factors like weather and urban infrastructure significantly impact localization accuracy. System integration poses another significant challenge, necessitating seamless interaction between localization systems and other vehicle control systems for real-time decision-making. Emerging trends and potential solutions to these challenges are discussed, highlighting advancements in sensor technology, AI, machine learning, and 5G communications. These developments promise to overcome existing limitations and enhance the accuracy and reliability of AV localization. The integration of localization systems with smart city infrastructures and the implementation of collaborative technologies like V2X communication are speculated to further augment AV capabilities. In conclusion, the paper emphasizes that the future of AV localization is intrinsically linked to the continuous evolution of technologies and their integration. Addressing current challenges and harnessing emerging trends are pivotal for the advancement of AVs, steering us toward a future of autonomous transportation characterized by increased safety, efficiency, and reliability.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions

The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.

Navigating the future: A comprehensive survey of localization systems in autonomous vehicles

&lt;p&gt;This survey paper provides an in-depth analysis of localization systems for autonomous vehicles (AVs), a cornerstone technology crucial for the safe and efficient operation of AVs. The paper encompasses a comprehensive examination of various localization technologies, their comparative analysis, the challenges they face, and the emerging trends shaping their future. We begin by exploring the principal technologies employed in AV localization: GPS, LiDAR, radar, cameras, and ultrasonic sensors. Each technology is scrutinized to understand its strengths, weaknesses, and applicability in different environmental contexts. GPS offers broad geographical positioning but struggles with precision in dense urban areas. LiDAR provides high-resolution mapping but is hindered by adverse weather conditions. Radar ensures reliability in poor visibility but lacks the finer details provided by optical systems. Cameras offer rich visual data but are dependent on lighting conditions, and ultrasonic sensors, while effective for close-range detection, are limited to low-speed applications. The paper then presents a comparative analysis of these technologies against key performance metrics such as accuracy, reliability, latency, scalability, and cost-effectiveness. This analysis is critical in understanding the suitability of each technology for various driving scenarios, from congested urban streets to open highways and in diverse weather conditions. Challenges in AV localization are multifaceted, encompassing technological, environmental, and system integration issues. Technological challenges include sensor limitations and computational constraints, while environmental factors like weather and urban infrastructure significantly impact localization accuracy. System integration poses another significant challenge, necessitating seamless interaction between localization systems and other vehicle control systems for real-time decision-making. Emerging trends and potential solutions to these challenges are discussed, highlighting advancements in sensor technology, AI, machine learning, and 5G communications. These developments promise to overcome existing limitations and enhance the accuracy and reliability of AV localization. The integration of localization systems with smart city infrastructures and the implementation of collaborative technologies like V2X communication are speculated to further augment AV capabilities. In conclusion, the paper emphasizes that the future of AV localization is intrinsically linked to the continuous evolution of technologies and their integration. Addressing current challenges and harnessing emerging trends are pivotal for the advancement of AVs, steering us toward a future of autonomous transportation characterized by increased safety, efficiency, and reliability.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;