Actual Scene Research Articles

Non-contact monitoring of human cardiorespiratory activity during sleep using FMCW millimeter wave radar

Millimeter wave radar can be utilized for non-contact, long-term, and accurate health monitoring. This paper presents an application for vital signs detection including respiratory rate (RR) and heart rate (HR) during sleep, as well as facilitating rapid detection and extended recording of apnea syndrome. This paper employs frequency spectrum analysis for RR estimation and develops an algorithm of template matching estimation (TME) for real-time, accurate HR estimation by comparing radar signal fragments to a template with a frequency signature. The time required for HR estimation is reduced to 1 s. In addition, a neural network-based model was developed for detecting the apnea events with the accuracy of 93.60% and calculates the duration in seconds. The system underwent testing using irregular analog signals of vital signs. In the actual scene testing, the accuracy of the RR estimation is 90.58%, and 90.25% for HR estimation.

Robotic MAG welding defects and quality assessment with a defect threshold decision model-driven method

The use of machine vision and deep learning methods for online monitoring and evaluation of welding quality during the welding process and timely detection and correction of welding defects is essential for intelligent welding manufacturing. In this paper, with the goal of online monitoring of the welding quality of the robotic arc welding for the excavator boom, we established a multi-source sensing system for robotic arc welding processes. We have developed various feature extraction algorithms for extracting information about welding pools, arc sounds, currents, voltages, and other features. We proposed to extract welding pool image features using Res2-MobileNetV3 to obtain a 12-dimensional welding pool feature, which enabled good inter-class discrimination among different welding defects. We designed a welding defect threshold decision (DD) strategy model using the responsive machine learning and lightweight network Res2-MobileNetV3 method, achieving a welding defect recognition accuracy of 96.59%. Finally, we tested the accuracy and reliability of the welding defect recognition model on an actual welding scene for the robotic welding of the excavator boom. It provided valuable scientific methods and technological approaches for intelligent welding in complex scenes.

Perspective Transform-based Depth Estimation of Monocular Camera for Electrocution Threat Determination of Construction Machinery

Abstract. This paper discusses the application of perspective transform-based monocular camera depth estimation method in monitoring the safety distance between construction machinery and power lines. The traditional distance measurement relies on manual operation, which is inefficient and not precise enough, while the monocular depth estimation method based on deep learning can improve the accuracy, but faces high equipment cost and relies on a large amount of training data. In contrast, the depth estimation method based on perspective transformation proposed in this paper utilizes mathematical and physical principles to establish a mathematical model between the pixel distances of an image and the actual scene distances through perspective projection theory, which simplifies the hardware requirements and reduces the data dependence and improves the computational efficiency and applicability. Through experimental validation under different construction scenes and exposure conditions, the method demonstrates high accuracy and robustness, proving its practicality in construction safety management. This research provides a new technical direction and practical application possibility in the field of intelligent monitoring and 3D reconstruction.

Physical modeling of organics-contaminated groundwater remediation by ozone micro-nano-bubbles enhanced oxidation

Ozone micro-nano-bubbles (MNBs) enhanced oxidation is a promising in-situ remediation technology for organics-contaminated groundwater. The transport behavior of MNBs and contaminant removal effect in actual groundwater scene is not yet comprehensively understood, although one-dimensional column experiments and batch tests were conducted to investigate the migration of MNBs and contaminant degradation in previous studies. Here, a novel two-dimensional (2D) physical modeling facility was developed, which can characterize the tempo-spatial distribution of MNBs and contaminant in groundwater. The facility was used to explore the migration of MNBs under different hydraulic gradients and the removal of methyl orange, a representative organic pollutant, using ozone MNBs. The experimental results show that groundwater flow velocity has a significant influence on the migration behavior of MNBs. A model including groundwater flow, MNBs transport, and dissolved gas transport can well capture the migration and distribution of MNBs under different hydraulic gradients. The longitudinal and transverse dispersivities of the MNBs were calculated to be 1.14 cm and 0.18 cm, respectively. MNBs are able to migrate with groundwater flow to long distances and exhibit strong dispersion in the lateral and longitudinal directions. The increase in hydraulic gradient greatly enhances the transport of MNBs in groundwater and relieves the deposited MNBs on the surface of porous media. At a hydraulic gradient increasing from 0.033 to 0.1, the range of influence of MNBs increased from about 80 cm × 40 cm to 140 cm × 39 cm after 36 h of injection of oxygen MNBs water. The methyl orange in groundwater is effectively removed by ozone MNBs enhanced oxidation, showing the remarkable remediation ability of ozone MNBs. The area of influence and pollutant removal of the ozone MNBs expanded with injection, and the area of influence was approximately 48 cm × 30 cm after 48 h of treatment. We also discuss the effect of groundwater matrix on ozone MNB migration and degradation of contaminants. The 2D model tests illustrate the mechanism of MNB migration and indicate that ozone MNBs enhanced oxidation technology has great potential in the remediation of organics-contaminated groundwater.

Centrifugation-modulated uniform antibacterial paper with well-interconnected hierarchical pores for flexible high-sensitivity humidity monitoring

Humidity-sensitive electrode architecture with high uniformity and enriched open pores is essential and crucial to the high-performance humidity monitoring. However, challenges remain pertaining to manufacturing consistency, remarkable water trapping and superior electrical conductivity. Herein, we demonstrated a facile and controllable centrifugal casting strategy for consistently constructing flexible antibacterial graphene oxide (GO)-silver nanowire (AgNW) humidity sensor, with ample well-interconnected hierarchical pores built by uniformly-distributed AgNWs and orientated GO nanosheets. The unique architectural features facilitated highly efficient transport and high-level trapping of water molecules, and meanwhile guaranteed continuous conductive network for accelerating electrochemical reactions of water molecules trapped inside the device, thereby boasting impressive humidity-sensing capability with a wide sensing range (7.6–95.6 % relative humidity (RH)), a low detection limit (3.8 % RH), good long-time stability (more than 100 days), exceptional folding durability (up to 1000 times), superior selectivity and sensitive monitoring in actual scenes. This work opens a promising route for architecting advanced humidity sensor with prominent monitoring ability toward future uses.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

Rotor proliferation promotes high-brightness AIE of iridium emitter accomplishing high-contrast luminous imaging of latent fingerprints to level 3 details

Luminous imaging of latent fingerprints (LFPs) necessitates the possession of high-brightness aggregation-state luminescence by developers to ensure sufficient imaging contrast and resolution. A novel strategy involving incremental rotor modification is presented for AIE activation of the iridium developer. The rotor proliferation prominently improves the rotational activity of groups and facilitates high-efficiency RIM, thereby prompting the AIE activation of iridium developer with high luminous efficiency. Subsequently, a prompt, high-contrast, and robust LFP imaging protocol is developed utilizing the high-brightness AIE-active iridium developer. This innovative protocol realizes the luminous imaging and quantification of microscopic features in fingerprint ridges and furrows, including ridge widths, edge morphology of ridges, included angles, pores, and pore pitches with exceptional imaging contrast and refined detail resolution. Moreover, it allows for accurate identification of individual traits across diverse substrates without any pre-/post-processing to LFPs. The high-brightness AIE-active iridium developer provides outstanding aging resistance to developed fingerprints, thereby strongly supporting the acquisition, transfer, and preservation of fingerprint evidence. The luminous imaging protocol of LFPs based on high-brightness AIE exhibits robust adaptability to actual scenes and offers a premium scheme for facilitating forensic investigation.

SSRLM: A self-supervised representation learning method for identifying one ship with multi-MMSI codes

It is of great practical significance for maritime safety and shipping development to achieve “One Ship with One Maritime Mobile Service Identity (MMSI) Code” in accordance with regulations. However, the current “One Ship with Multiple MMSI Codes” violation identification method lacks practicability and generalization. Aiming at the these problems, this paper proposes a self-supervised representation learning model for the “One Ship with Multiple MMSI Codes” abnormal behavior recognition task, named SSRLM. Firstly, we construct a new dataset about “One Ship with Multiple MMSI Codes”, named HN_MulMI. Secondly, the SSRLM model learns the contextual interdependencies among different trajectory features in the HN_MulMI dataset through an unsupervised pre-training scheme, and extracts a dense vectorial representation of the ship’s motion trajectories. Finally, by using the unique characteristics of the “One Ship with Multiple MMSI Codes” trajectory sequence, the SSRLM model captures its feature dependencies through a fine-tuning scheme to accomplish the abnormal behavior detection task. Experimental results in the two scenarios of HN_MulMI test set demonstrate that the proposed model outperform the state-of-the-art models in recent years, the average precision, recall and F1-score rates is up to about 99.26% and 93.5%. Meanwhile, we also conducted comparative experiments on three public datasets to verify the generalization performance, achieving 90.31%, 93.2% and 95.14% results in the F1 score evaluation indicators, further confirming the good recognition performance of the SSLLM model. Finally, our model has been applied to the actual scene and achieved good results.

Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method.

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.

The highly efficient and stable near-infrared phosphor Ca3MgSnGe3O12: Cr3+ for multifunctional application

The investigation of efficient and stable near-infrared (NIR) luminescent materials is crucial for rapidly developing near-infrared spectroscopic techniques. Herein, NIR phosphors Ca3MgSnGe3O12: Cr3+ with ultra thermal stability and high internal quantum yield (IQY) are explored. The emission intensity remained at 91.75 %@423 K of room temperature and the IQY/EQE reaches 91.41 %/34.04 % after doping the H3BO3 flux agent for optimization. Moreover, a single luminescence center was identified by cryogenic photo luminescence spectroscopy (PL) from 4 K to 300 K and lifetime decay curve. Importantly, NIR light can easily removes all kinds of background interference that cannot be easily eliminated by visible light, which is not only widely used in night vision and biological tissue penetration, but also has higher application prospects in NIR fingerprint detection. Moreover, it clearly displays high level of fingerprint details under high temperature conditions and after water immersion, thus it is perfectly suited for using in Automatic Fingerprint Identification System (AFIS) systems. Therefore, Ca3MgSnGe3O12: Cr3+ NIR pc-LEDs have great potential for latent fingerprint detection at actual crime scenes.

Citrus recognition in orchard scene based on modified HSV-morphology method

The orchard has always been an important scene for citrus pickers, and the existence of factors such as leaf occlusion and color similarity always lead to the difficulty of robot recognition. Based on a citrus image dataset collected from actual orchard scenes, we extract image features and build a mathematical model to identify and count the number of oranges in each image, and show the distribution of apples in the entire dataset. Firstly, this paper establishes a model based on HSV method, focusing on recognizing yellow citrus. Secondly, with the further analysis of the data set, it is found that the cyan citrus exists. Therefore, this paper defines the empirical HSV range of two kinds of citrus at the same time and introduces contour processing to optimize the model. In order to accurately distinguish the immature citrus from the leaves with similar colors and the citrus occluded by leaves, this paper uses more detailed color threshold setting and morphological operation, and makes the eccentricity of the contour less than 0.85. At the same time, considering the size and overlap of citrus, morphological operations such as dilation and erosion and contour detection techniques are applied to further improve the recognition ability of the model. Finally, the experimental results show that the average accuracy of the established model reaches 92.1%, which has a good recognition effect on citrus.

Detection-first tightly-coupled LiDAR-Visual-Inertial SLAM in dynamic environments

To address the challenges posed by the dynamic environment for Simultaneous Localization and Mapping (SLAM), a detection-first tightly-coupled LiDAR-Visual-Inertial SLAM incorporating lidar, camera, and inertial measurement unit (IMU) is proposed. Firstly, the point cloud clustering with semantic labels are obtained by fusing image and point cloud information. Then, a tracking algorithm is applied to obtain the information of the motion state of the targets. Afterwards, the tracked dynamic targets are utilized to eliminate extraneous feature points. Finally, a factor graph is used to jointly optimize the IMU pre-integration, and tightly couple the laser odometry and visual odometry within the system. To validate the performance of the proposed SLAM framework, both public datasets (KITTI and UrbanNav) and actual scene data are tested. The experimental results show that compared with LeGO-LOAM, LIO-SAM and LVI-SAM for public dataset, the root mean squared error (RMSE) of proposed algorithm is decreased by 44.56 % (4.47 m) and 4.15 % (4.62 m) in high dynamic scenes and normal scenes, respectively. Through actual scene data, the proposed algorithm mitigates the impact of dynamic objects on map building directly.

A virtual-reality spatial matching algorithm and its application on equipment maintenance support: System design and user study

Equipment maintenance support is an important technical measure to maintain the equipment’s expected performance. However, the current maintenance supports are mainly completed by maintainers under the guidance of technical manual or additional experts, which may be insufficient for some advanced equipment with rapid update rate and complex inner structure. The rising technology of augmented reality (AR) provides a new solution for equipment maintenance support, while one of the key issues limiting the practical application of AR in maintenance field is the spatial matching issue between virtual space and reality space. In this paper, a virtual-reality spatial matching algorithm is designed to accurately superimpose the virtual information to the corresponding actual scene on the AR glasses. In this algorithm, two methods are proposed to help achieve the stable matching of virtual space and reality space. In detail, to obtain the saliency map with less background interference and improved saliency detection accuracy, a saliency detection method is designed based on the super-pixel segmentation. To deal with the problems of uneven distribution on the feature points and weak robustness to the light changes, a feature extraction and matching method is proposed for acquiring the feature point matching set with the utilization of the obtained saliency map. Finally, an immersive equipment maintenance support system (IEMSS) is developed based on this spatial matching algorithm, which provides the maintainers with immediate and immersive guidance to improve the efficiency and safety in the maintenance task, as well as offers maintenance training for inexperienced maintainers with expanded virtual information in case of limited experts. Several comparative experiments are implemented to verify the effectiveness of proposed methods, and a user study of real system application is carried out to further evaluate the superiority of these methods when applied in the IEMSS.

Elementary School Students' Explanatory Text Learning Innovation Using the Problem Based Learning Model Assisted by Diorama Media

The lack of students' writing skills and limited learning facilities and media further worsen the writing quality of students in elementary school (SD). Explanatory text is text that informs the process of something occurring, whether natural or social phenomena. At the stage of understanding and writing the text, students find it difficult due to the lack of information and knowledge possessed by the students and the teacher as facilitator. Model problem based learning is a learning model that uses real problems in its learning so that it can train students to develop problem-solving skills. This model can trigger students' thinking methods in presenting information in an explanatory text that is in accordance with the structure of the explanatory text. Based on the analysis of the advantages of diorama media, it can be concluded that students' ability to write explanatory texts increases in accordance with the concept of diorama media, which is a mini three-dimensional view that aims to depict the actual scene. This is because in this model students are required to work together in groups regarding dioramas that have been observed, so that students have a lot of material and information to write explanatory texts so that learning becomes different and fun. Keywords: Explanatory text, problem based learning, diorama media

A Study on the Thermodynamic Response of Double-Armed Thin-Walled Piers under an FRP Anti-Collision Floating Pontoon Fire

Abstract: As a potential fire scenario for bridge structures, the safety impact of an FRP anti-collision floating pontoon fire on bridge structures cannot be ignored. Taking the FRP anti-collision floating pontoon fire that occurred in a continuous rigid-frame bridge as the engineering background, the damage condition of the actual bridge fire scene was first investigated. In addition, FDS 5.3 software was used to simulate the FRP anti-collision floating pontoon fire scenario. Furthermore, the thermal–structural coupling method was used to investigate the thermodynamic response of double-armed thin-walled piers under fire. The results show that the FRP anti-collision floating pontoon fire causes localized concrete carbonization and spalling on the surface of the P2 pier, and the FRP anti-collision floating pontoons are largely destroyed. The fire has the greatest impact on the P2-1 pier, with the highest temperature of 667 °C on the windward side and the highest temperature of 326 °C on the leeward side. The temperature impact range is 6 m above the bearing platform, and the maximum damage depth of pier body concrete is 84.58 mm. The deformation and stress of the P2 pier under fire do not show significant changes and do not exceed the allowable limits for structural deformation and material stress. Therefore, the impact of this fire accident on the structural safety of the continuous rigid-frame bridge is minor. This study's results provide reliable guidance for the fire safety assessment and post-fire structural repair of the continuous rigid-frame bridge.

Designing and evaluation of a mixed reality system for crime scene investigation training: a hybrid approach

Police investigation in real-life crime scenes is an essential aspect of forensic science education. However, the practicality of bringing young investigators to actual crime scenes is often hindered by the costs and challenges involved. In order to overcome these obstacles, new technologies such as mixed reality (MR) are being explored as potential solutions. MR technology offers an interactive and cost-effective way to simulate real-life crime scenes, providing a valuable training experience for young investigators. This paper presents a novel design of a MR system using Microsoft HoloLens 2.0, which is tailored to work in a spatial 3D scanned and reconstructed crime scene using FARO point cloud 3D scanner X130 blended with photogrammetry techniques. The system was developed through the lens of Experiential Learning Theory and designed using a participatory approach, providing a cost-effective solution to help trained Kuwaiti police officers enhance their investigative skills. In order to evaluate the system’s user experience and user interaction, the Questionnaire of User Interaction Satisfaction and User Experience Questionnaire were utilised. Forty-four young police officers evaluated the system. Police students showed positive levels of satisfaction with user interaction and overall user experience with minimal negative feedback. Female students showed higher satisfaction with the overall impression compared to male students. Based on the positive feedback regarding the system expansion, the system will be taken into the commercialisation stage in the future to be provided as an essential tool for crime scene education and investigation practices.

Enhancing Fire Safety Education Through Immersive Virtual Reality Training with Serious Gaming and Haptic Feedback

Over the past decades, China has faced an increasingly severe fire issue, resulting in significant human and financial losses. Consequently, the importance of enhancing fire safety education for citizens has become more pronounced. However, due to safety risks in actual fire scenes, the practical use of firefighting equipment was often learned passively through text or video resources, posing challenges in ensuring users can proficiently operate actual equipment. Virtual reality (VR) emerged as a promising technology to provide users with a low-risk environment to gain hands-on experience with firefighting equipment. Therefore, this study aimed to assess the effectiveness of a specially designed VR game-based firefighting extinguishing equipment training (FEET) that integrated VR with serious gaming elements. The study evaluated the effectiveness of FEET through a comparison between high (HI) and low (LI) immersiveness VR and a non-immersive (NI) setting as a baseline. The study incorporated haptic feedback mechanisms, such as audio and vibration, to enhance user immersiveness within the HI setting. The focus of the study was on evaluating knowledge acquisition, user experiences, and interaction preferences for the proposed FEET design. The study adopted three distinct learning sequences to mitigate the VR halo effects. The results of the study indicated that both VR-based learning methods yielded outstanding user experiences that significantly surpassed the NI setting. Furthermore, the study showed that haptic feedback with richer game contents further enhanced user experiences, underscoring the importance of simulating realistic sensing in improving learning outcomes.

Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics.

Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.

Automatic extraction and 3D modeling of real road scenes using UAV imagery and deep learning semantic segmentation

ABSTRACT The extraction of roads from UAV images is challenged by lighting, noise, occlusions, and similar non-road objects, making high-quality road extraction difficult. To addressing these issues, this study proposes an enhanced U-Net network to automate the extraction and 3D modeling of real road scenes using UAV imagery. Initially, a cascaded atrous spatial pyramid module was integrated into the encoder to capitalize on global context information, thereby refining the fuzzy segmentation outcomes. Subsequently, a module for augmenting road feature extraction was added within the channel, and a spatial attention mechanism was introduced in the decoder to enhance edge clarity. Experimental results demonstrated that this model captures more road information compared to mainstream networks and effectively incorporates topological structure perception for road extraction in complex scenarios, thus improving road connectivity. The model achieved an F1 score and mean Intersection over Union (mIoU) of 85.6% and 81.2%, respectively, on UAV images of road scenes – marking improvements of 3.9% and 3.4% over the traditional U-Net model, thereby exhibiting superior automatic road extraction capabilities. Ultimately, the model facilitated refined modeling and visual analysis of road scenes, achieving high overall accuracy and detailed local restoration of the actual scene.

Grounding rod hanging and removing robot with hand-eye self-calibration capability in substation

In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions.