Processing Time Research Articles

A grey relational analysis of the micro arc oxidation process parameters and their effects on Ti-6Al-7Nb coating performance and corrosion resistance for biomedical uses

Abstract In current investigation micro arc oxidation of Ti6Al7Nb alloy was done to improve its surface properties and corrosion resistance. Mixture of Na2SiO3, Na3PO412H2O and KOH is used as electrolyte. MAO treated Ti6Al7Nb specimens were examined using x-ray diffraction (XRD), energy dispersive spectroscopy (EDS) and scanning electron microscopy (SEM) to examine their morphology and phase composition. It was observed that electrolyte composition is simultaneously included in the growing oxide layer during MAO process. From electrochemical study it was found that corrosion resistance of the Ti6Al7Nb increases during EIS testing in 0.9% NaCl solution. It was found that frequency, duty cycle, current and processing time effect the surface roughness, thickness, hardness and corrosion resistance of coating. Out of above mention parameters frequency and duty cycle has major impact on performance parameters. The objective of current investigation is to find out effects MAO process parameters on coating performance parameters such as coating thickness, hardness, surface roughness and corrosion resistance. At duty cycle of 50%, frequency 500 Hz, current 300 mA and processing duration 7.5 min, highest coating thickness 32.96 μm and surface roughness 3.3680 μm was obtained. Process parameters have the influence on pore size, biggest average pore size 3.8519 μm was obtained at duty cycle of 50%, frequency 500 Hz, current 300 mA and processing duration 7.5 min. Grey relational analysis is done to determine which process variable has the most influence on performance parameters. From grey relational analysis technique, it was observed that duty cycle 50%, frequency 500 Hz, current 300 mA, and processing time 7.5 min are ideal process parameters for higher coating thickness, hardness, surface roughness and better corrosion resistance. From grey relation analysis it was also found that frequency has most significant impact on performance parameters after that duty cycle, then current and at last processing time.

Welding Defect Monitoring Based on Multi-Scale Feature Fusion of Molten Pool Videos

Real-time quality monitoring through molten pool images is a critical focus in researching high-quality, intelligent automated welding. However, challenges such as the dynamic nature of the molten pool, changes in camera perspective, and variations in pool shape make defect detection using single-frame images difficult. We propose a multi-scale fusion method for defect monitoring based on molten pool videos to address these issues. This method analyzes the temporal changes in light spots on the molten pool surface, transferring features between frames to capture dynamic behavior. Our approach employs multi-scale feature fusion using row and column convolutions along with a gated fusion module to accommodate variations in pool size and position, enabling the detection of light spot changes of different sizes and directions from coarse to fine. Additionally, incorporating mixed attention with row and column features enables the model to capture the characteristics of the molten pool more efficiently. Our method achieves an accuracy of 97.416% on a molten pool video dataset, with a processing time of 16 ms per sample. Experimental results on the UCF101-24 and JHMDB datasets also demonstrate the method’s generalization capability.

Increased efficiency of peripheral nerve regeneration using supercritical carbon dioxide-based decellularization in acellular nerve graft

Acellular nerve grafts (ANGs) are a promising therapeutic for patients with nerve defects caused by injuries. Conventional decellularization methods utilize a variety of detergents and enzymes. However, these methods have disadvantages, such as long processing times and the presence of detergents that remain on the graft. In this study, we aimed to reduce process time and minimize the risks associated with residual detergents by replacing them with supercritical carbon dioxide (scCO2) and compared the effectiveness to Hudson’s decellularization method, which uses several detergents. The dsDNA and the expression of MHC1 and 2 were significantly reduced in both decellularized groups, which confirmed the effective removal of cellular debris. The extracellular matrix proteins and various factors were found to be better preserved in the scCO2 ANGs compared to the detergent-ANGs. We conducted behavioral tests and histological analyses to assess the impact of scCO2 ANGs on peripheral nerve regeneration in animal models. Compared with Hudson’s method, the scCO2 method effectively improved the efficacy of peripheral nerve regeneration. Therefore, the decellularization method using scCO2 is not only beneficial for ANG synthesis, but it may also be helpful for therapeutics by enhancing the efficacy of peripheral nerve regeneration.

Impedance Spectroscopy for Bacterial Cell Monitoring, Analysis, and Antibiotic Susceptibility Testing.

Conventional approaches for bacterial cell analysis are hindered by lengthy processing times and tedious protocols that rely on gene amplification and cell culture. Impedance spectroscopy has emerged as a promising tool for efficient real-time bacterial monitoring, owing to its simple, label-free nature and cost-effectiveness. However, its limited practical applications in real-world scenarios pose a significant challenge. In this review, we provide a comprehensive study of impedance spectroscopy and its practical utilization in bacterial system measurements. We begin by outlining the fundamentals of impedance theory and modeling, specific to bacterial systems. We then offer insights into various strategies for bacterial cell detection and discuss the role of impedance spectroscopy in antimicrobial susceptibility testing (AST) and single-cell analysis. Additionally, we explore key aspects of impedance system design, including the influence of electrodes, media, and cell enrichment techniques on the sensitivity, specificity, detection speed, concentration accuracy, and cost-effectiveness of current impedance biosensors. By combining different biosensor design parameters, impedance theory, and detection principles, we propose that impedance applications can be expanded to point-of-care diagnostics, enhancing their practical utility. This Perspective focuses exclusively on ideally polarizable (fully capacitive) electrodes, excluding any consideration of charge transfer resulting from Faradaic reactions.

Automated quality control analysis for American College of Radiology (ACR) digital mammography (DM) phantom images.

To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images. Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses). The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15mm. Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time.

The Validity and Usability of Markerless Motion Capture and Inertial Measurement Units for Quantifying Dynamic Movements

ABSTRACT Purpose Motion capture technology is quickly evolving providing researchers, clinicians, and coaches with more access to biomechanics data. Markerless motion capture and inertial measurement units (IMUs) are continually developing biomechanics tools that need validation for dynamic movements before widespread use in applied settings. This study evaluated the validity of a markerless motion capture, IMU, and red, green, blue, and depth (RGBD) camera system as compared to marker-based motion capture during countermovement jumps, overhead squats, lunges, and runs with cuts. Methods Thirty adults were recruited for this study (sex: 18 females, 12 males; age: 25.4 ± 8.6 years; height: 1.71 ± 0.08 m; weight: 71.6 ± 11.5 kg). Data were collected simultaneously with four motion capture technologies (i.e., Vicon – marker-based, Theia/Optitrack – markerless, APDM Opals – IMUs, and Vald HumanTrak – RGBD camera). System validity for lower and upper body joint angles was evaluated using bias, root mean squared error (RMSE), precision, maximum absolute error, and intraclass correlation coefficients. System usability was descriptively analyzed. Results Overall, markerless motion capture had the highest validity (sagittal plane RMSE: 3.20-15.66°; frontal plane RMSE: 2.12-9.14°; transverse plane RMSE: 3.160-56.61°), followed by the IMU system (sagittal plane RMSE: 8.11-28.37°; frontal plane RMSE: 3.26-16.98°; transverse plane RMSE: 5.08-116.75°), and lastly the RGBD system (sagittal plane bias: 0.55-129.48°; frontal plane bias: 1.35-52.06°). Conclusions Markerless motion capture and IMUs have moderate validity for joint kinematics, while the RGBD system did not have adequate validity. Markerless systems have lower data processing time, require moderate technical expertise, but have high data storage size. IMUs are easier to use, can collect data in any location, but require participant set-up. Overall, individuals using motion capture should consider the specific movements, testing locations, and technical expertise available prior to selecting a system.

Defective Pennywort Leaf Detection Using Machine Vision and Mask R-CNN Model

Demand and market value for pennywort largely depend on the quality of the leaves, which can be affected by various ambient environment or fertigation variables during cultivation. Although early detection of defects in pennywort leaves would enable growers to take quick action, conventional manual detection is laborious and time consuming as well as subjective. Therefore, the objective of this study was to develop an automatic leaf defect detection algorithm for pennywort plants grown under controlled environment conditions, using machine vision and deep learning techniques. Leaf images were captured from pennywort plants grown in an ebb-and-flow hydroponic system under fluorescent light conditions in a controlled plant factory environment. Physically or biologically damaged leaves (e.g., curled, creased, discolored, misshapen, or brown spotted) were classified as defective leaves. Images were annotated using an online tool, and Mask R-CNN models were implemented with the integrated attention mechanisms, convolutional block attention module (CBAM) and coordinate attention (CA) and compared for improved image feature extraction. Transfer learning was employed to train the model with a smaller dataset, effectively reducing processing time. The improved models demonstrated significant advancements in accuracy and precision, with the CA-augmented model achieving the highest metrics, including a mean average precision (mAP) of 0.931 and an accuracy of 0.937. These enhancements enabled more precise localization and classification of leaf defects, outperforming the baseline Mask R-CNN model in complex visual recognition tasks. The final model was robust, effectively distinguishing defective leaves in challenging scenarios, making it highly suitable for applications in precision agriculture. Future research can build on this modeling framework, exploring additional variables to identify specific leaf abnormalities at earlier growth stages, which is crucial for production quality assurance.

Deep reinforcement learning driven trajectory-based meta-heuristic for distributed heterogeneous flexible job shop scheduling problem

As the production environment evolves, distributed manufacturing exhibits heterogeneous characteristics, including diverse machines, workers, and production processes. This paper examines a distributed heterogeneous flexible job shop scheduling problem (DHFJSP) with varying processing times. A mixed integer linear programming (MILP) model of the DHFJSP is formulated with the objective of minimizing the makespan. To solve the DHFJSP, we propose a deep Q network-aided automatic design of a variable neighborhood search algorithm (DQN-VNS). By analyzing schedules, sixty-one types of scheduling features are extracted. These features, along with six shaking strategies, are used as states and actions. A DHFJSP environment simulator is developed to train the deep Q network. The well-trained DQN then generates the shaking procedure for VNS. Additionally, a greedy initialization method is proposed to enhance the quality of the initial solution. Seven efficient critical path-based neighborhood structures with three-vector encoding scheme are introduced to improve local search. Numerical experiments on various scales of instances validate the effectiveness of the MILP model and the DQN-VNS algorithm. The results show that the DQN-VNS algorithm achieves an average relative percentage deviation (ARPD) of 3.2%, which represents an approximately 88.45% reduction compared to the best-performing algorithm among the six compared, with an ARPD of 27.7%. This significant reduction in ARPD highlights the superior stability and performance of the proposed DQN-VNS algorithm.

Performance Evaluation of Refractive Index Biosensor in THz Regime for Clinical Applications: A Simulation Approach

Abstract A novel innovative HC-PCF sensor model in THz regime is introduced integrated with an optimization approach. The suggested sensor architecture provides crucial advantages precise identification of healthy and ill tissues in the healthcare industry. The HC-PCF, meticulously constructed with specific dimensions, significantly increases the sensor sensitivity and specificity to 99.37% and 99.75% respectively. Healthcare industries are at the core of investigations and are undoubtedly crucial to modernize the prognosis procedures. It is a discipline that is continually expanding and searching for new approaches to raise the standard for efficacy, sensitivity, and accuracy. Recently, THz PCF has emerged with incredible potential in all the areas of biomedical applications. The importance in using THz sensors in this research is to detect the ill tissues, an important component in the categorizing diabetes. The integrated sensor architecture provides higher level of sensitivity, with a confinement loss of 0.05 in 0.23s processing time for a RI range of 1.28-1.39. This research highlights the capability of combining PCF with optimization to improvise the healthcare industry, offering an economical and efficient diagnostic solution across the fields.

Optimization and modelling of bioethanol production by the fermentation of CCN‐51 cocoa mucilage using the sequential simplex method and the modified Gompertz model

AbstractThis work deals with the optimization of bioethanol production through a fermentation process of CCN‐51 cocoa mucilage, based on increased concentrations of the Saccharomyces cerevisiae yeast. Cocoa mucilage, considered biomass waste, was selected for its high productivity and the large volumes generated in the cocoa industrial chain in Ecuador. The optimization of the fermentation process was performed using the sequential simplex method with two variables, and the results were experimentally confirmed by quantifying bioethanol through the microdiffusion method. The best operational conditions corresponded to a temperature of 35°C and a pH of 4. Regarding the concentration of yeast, it was found that the optimal value was 8 g/L, since lower concentrations led to low productivities, while higher concentrations resulted in inadequate functioning of the bioreactor. The best results reached a productivity of 1.35 ± 0.04 g/L · h and a maximum bioethanol concentration of 28.3 ± 0.8 g/L for a processing time of 21 h. The production of bioethanol was modelled using the modified Gompertz equation and simulated in MATLAB®, yielding a bioethanol production rate of 2.42 g/L · h with a correlation coefficient (R2) of 0.95. These results contribute to the knowledge of bioethanol production using cocoa mucilage and seek to add a positive value to this residue, whose management and final disposition have both undesirable environmental and economic effects.

Improving production sequencing in the age of Industry 4.0: A mathematical launching model approach

Purpose: To enhance enterprise efficiency, this study examines the pivotal role of operations management in optimizing the use of materials, technology, equipment, and personnel, especially within the transformative framework of Industry 4.0.Design/methodology/approach: This article investigates operational preparation in the context of Industry 4.0. Through questionnaires and interviews with stakeholders, problems related to operational preparation were identified. To address these problems, a launching model was developed for production systems. The model considers several parameters important for the production process, including business goals, customer needs, and environmental conditions that impact enterprise performance and profit.Findings: The model determines the importance of jobs to be performed in a certain order to optimize production sequence. The proposed parameters are included in a mathematical-algorithmic launching model based on categorical levels related to the production process, such as profit, delivery times, processing times, total number of technological operations, product types, materials, required quality, product complexity, and resource use. The model was developed based on observations and investigations conducted in Kosovo's enterprises on operations research, and it has the potential to significantly improve production efficiency and profitability in the Industry 4.0 era.Practical implications: The findings of this study have practical implications for operations management within the Industry 4.0 framework, proposing a launching model designed to optimize production processes and enhance efficiency.Originality/value: The originality and value of this research lie in the development of a mathematical-algorithmic launching model that addresses operational preparation in the Industry 4.0 context, taking into account various crucial parameters.

Functional performance comparison of long cane and secondary electronic travel aids for mobility enhancement

This study investigated the impact of secondary electronic travel aids (ETAs) on the mobility performance of individuals with profound visual impairment, specifically focusing on object detection and obstacle avoidance tasks. Seven experienced long-cane users were recruited for the study. The ETAs tested as secondary aids to the long cane included the MiniGuide, utilizing ultrasonic echo location, and the vOICe (Vision-to-Auditory Sensory Substitution Device), which translates visual information into auditory signals. Two software modes of the vOICe were examined: the standard contrast mode and a novel algorithm utilizing relative depth information. Notably, 30 min of familiarization was provided before the trials. Results indicated that while the use of the secondary ETAs led to increased awareness of the surrounding environment, it also resulted in longer processing times and more hesitations, compared with using the long cane alone. Participants demonstrated improved object detection and larger detection and safety ranges during obstacle avoidance. The study also introduced the concept of margin of stability (MoS) as a novel metric for evaluating gait stability while using ETAs. Both MiniGuide and vOICe users exhibited consistent and stable movements, suggesting that these additional aids did not negatively impact overall gait stability. The findings highlight the trade-offs between efficiency and effectiveness when using secondary mobility aids, with longer training periods potentially mitigating these issues. In addition, the study emphasizes the need for ongoing research to optimize the design and training programmes for new ETAs. Future studies may explore the impact of extended training periods on mobility performance. Overall, this research contributes to a deeper understanding of how ETAs can enhance the mobility of individuals with visual impairments while also highlighting some of the challenges associated with their use. Optimizing ETA design and training programmes is crucial for maximizing benefits for individuals with impaired vision.

A novel route to produce metal or ceramic parts in space: local debinding and sintering of powdered filaments

AbstractIn-space manufacturing of polymer feedstocks has already been shown using the widely investigated filament extrusion additive manufacturing (AM) technology. Yet, polymers are only a small piece of the puzzle, and there is a growing demand to locally source metal and ceramic parts. In this manuscript, we propose a cost-effective method for in-orbit manufacturing of metal and ceramic multi-material components using highly packed powdered filaments, which need to be shaped, debinded, and sintered in sequential steps. Traditional debinding and sintering of material extrusion (MEX) AM parts are known to be time-consuming and require complex post-processing, often involving toxic debinding agents. To overcome this, a low-intensity infrared diode laser and an induction heater are coupled to a hybrid MEX system to allow full processing in situ, within the same volume. The results show that the main binder matrix can be removed across the 3D volume of the part via laser ablation of the polymeric mass, even for multi-material metal–ceramic composites. The sintered geometries further densify efficiently within the bulk due to the high-energy concentration of the induction sintering treatment, providing short processing times. Debinding and sintering locally, in the same machine, offer a simple and effective way to produce space hardware in situ, avoiding the use of consumables or part transportation to bulky equipment.

A Modified Bleaching Method for Multiplex Immunofluorescence Staining of FFPE Tissue Sections.

Multiplex immunofluorescence (mIF) staining plays an important role in profiling biomarkers and allows investigation of co-relationships between multiple biomarkers in the same tissue section. The Cell DIVE mIF platform (Leica Microsystems) employs an alkaline solution of hydrogen peroxide as a fluorophore inactivation reagent in the sequential staining, imaging, and bleaching protocol for use on FFPE sections. Suboptimal bleaching efficiency, degradation of tissue structure, and loss of antigen immunogenicity occasionally are encountered with the standard bleaching process. To overcome these impediments, we adopted a modified photochemical bleaching method, which utilizes an intense LED light exposure concurrent with the application of hydrogen peroxide. Repeated stain/bleach rounds with different antibodies were performed on breast tissue and other tissue sections. Residual signal after conventional bleaching and the modified technique were compared and tissue integrity and antigen immunogenicity were assessed. The modified technique effectively eliminates fluorescence signal from previous staining rounds and produces consistent results for multiple rounds of staining and imaging. With the modified method, photochemical treatments did not destroy tissue sub-cellular contents, and the tissue antigenicity was well preserved during the entire mIF process. Overall processing time was reduced from 36 to 30 hours in an mIF procedure with 8 rounds. With the conventional method, tissue quality was highly degraded after 8 rounds. The new technique allows reduced turn-around time, provides reliable fluorophore removal in mIF with excellent maintenance of tissue integrity, facilitating studies of the co-localization of multiple biomarkers in tissues of interest.

Development of A Workshop on the Topic of the Bionically Inspired Lightweight Design Method for 3D Printed Components

Additive manufacturing methods, particularly 3D printing, are widely utilized in research and engineering for crafting lightweight yet durable materials capable of withstanding substantial forces. Leveraging insights from biomechanical structures offers a deeper understanding of reinforcement techniques and optimal design strategies to enhance strength. This paper focuses on the development of a Draisine design, historically significant in Karlsruhe, as part of the BWSplus project "Drais3D-Trinational" workshop. Through a comprehensive analysis of bionic influences on lightweight design and 3D printing parameters, this study aims to create an optimal design framework for the workshop. Utilizing Computer-Aided Design (CAD) software such as SolidWorks and Creo Parametric, along with Finite Element Analysis using ANSYS R2023 Workbench, deformation and stress analysis are conducted. Investigation into 3D printing parameters, including infill patterns, temperature, support systems, and orientation, seeks to identify optimal solutions considering factors like processing time, robustness, and filament wastage. The objective is to explore the biomechanical influence on construction methods, concept design, and parameter construction in preparation for the Drais3D-Trinational workshop in March 2023. The expected outcomes include the presentation of two main designs with varied parameters, alongside analyses such as Finite Element Analysis and optimization of 3D printing parameters, emphasizing the role of bionic structures in defining the optimal Draisine design.

Uncovering echoic mechanisms in verbal irony comprehension

Abstract Whereas some studies suggest that ironic praise necessitates a longer processing time than ironic criticism, others posit that the two are processed at comparable speeds. We hypothesize that the presence of an echoic antecedent within the preceding context may at least partially account for these conflicting findings. To investigate this matter, we analyzed reading times and accuracy stemming from two types of contexts: echoic and non-echoic. Our results demonstrate that ironic criticism was judged to be more ironic in both echoic and non-echoic contexts, while ironic praise was rated as more ironic in an echoic context than in a non-echoic context. Additionally, echoing contexts facilitate the comprehension of ironic criticism, but cause ironic praise to be processed more slowly. There was also an observed asymmetry between the two forms of irony. Ironic criticism demonstrated high accuracy and was rated as more ironic than ironic praise. Furthermore, ironic criticism was read faster in an echoic context, whereas performance was similar in a non-echoic context for both types. These findings suggest that echoing context affects ironic criticism and ironic praise differently, implying that distinct mechanisms may be at work in understanding irony in echoic and non-echoic contexts.

An Automated Clubbed Fingers Detection System Based on YOLOv8 and U-Net: A Tool for Early Prediction of Lung and Cardiovascular Diseases

Background/Objectives: Lung and cardiovascular diseases are leading causes of mortality worldwide, yet early detection remains challenging due to the subtle symptoms. Digital clubbing, characterized by the bulbous enlargement of the fingertips, serves as an early indicator of these diseases. This study aims to develop an automated system for detecting digital clubbing using deep-learning models for real-time monitoring and early intervention. Methods: The proposed system utilizes the YOLOv8 model for object detection and U-Net for image segmentation, integrated with the ESP32-CAM development board to capture and analyze finger images. The severity of digital clubbing is determined using a custom algorithm based on the Lovibond angle theory, categorizing the condition into normal, mild, moderate, and severe. The system was evaluated using 1768 images and achieved cloud-based and real-time processing capabilities. Results: The system demonstrated high accuracy (98.34%) in real-time detection with precision (98.22%), sensitivity (99.48%), and specificity (98.22%). Cloud-based processing achieved slightly lower but robust results, with an accuracy of 96.38%. The average processing time was 0.15 s per image, showcasing its real-time potential. Conclusions: This automated system provides a scalable and cost-effective solution for the early detection of digital clubbing, enabling timely intervention for lung and cardiovascular diseases. Its high accuracy and real-time capabilities make it suitable for both clinical and home-based health monitoring.

Real-Time Freeway Crash Detection Framework using Connected Vehicle Waypoint Data

Accurate and timely detection of traffic accidents is crucial for transportation agencies seeking swift responses and effective traffic management, particularly on freeways where crash severity and traffic flow disruptions are amplified. While several methods have been developed using cameras or infrastructure-mounted sensors, these approaches may face challenges in geographical scalability. To address this issue, a novel approach has been developed that leverages real-time connected vehicle trajectories at a market penetration rate of 4%–9%. The method involves extracting five key features from individual journeys that sense the speed and acceleration fluctuations. The results demonstrate promising outcomes, achieving an overall accuracy of 63.5% with 4.1% false detections and 33.4% non-detections. This accuracy surpasses the speed-alone model using the same data by 24%. Furthermore, higher traffic volumes lead to even greater accuracy, exceeding 80% at level of service D and 90% at level of service E. The developed algorithm also exhibits precision in crash detection, with a mean latency of only 2.5 min after the actual incidents. The entire processing time may additionally add up to 1 min. The robustness of the model is tested for a single day on one of the interstates, providing promising results. This study highlights the potential of connected vehicle data in enhancing crash prediction methods and underlines its value for the safety and efficiency of future transportation systems.

A Comparative Study of Heuristic Algorithms to Solve Flow Shop Scheduling Problem with Fuzzy Processing Time

A Comparative Study of Heuristic Algorithms to Solve Flow Shop Scheduling Problem with Fuzzy Processing Time

Fast Motion State Estimation Based on Point Cloud by Combing Deep Learning and Spatio-Temporal Constraints

Moving objects in the environment have a higher priority and more challenges in growing domains like unmanned vehicles and intelligent robotics. Estimating the motion state of objects based on point clouds in outdoor scenarios is currently a challenging area of research. This is due to factors such as limited temporal information, large volumes of data, extended network processing times, and the ego-motion. The number of points in a point cloud frame is typically 60,000–120,000 points, but most current motion state estimation methods for point clouds only downsample to a few thousand points for fast processing. The downsampling step will lead to the loss of scene information, which means these methods are far from being used in practical applications. Thus, this paper proposes a motion state estimation method that combines spatio-temporal constraints and deep learning. It starts by estimating and compensating the ego-motion of multi-frame point cloud data and mapping multi-frame data to a unified coordinate system; then the point cloud motion segmentation model on the multi-frame point cloud is proposed for motion object segmentation. Finally, spatio-temporal constraints are utilized to correlate the moving object at different moments and estimate the motion vectors. Experiments on KITTI, nuScenes, and real captured data show that the proposed method has good results, with an average vector deviation of only 0.036 m and 0.043 m in KITTI and nuScenes under a processing time of about 80 ms. The EPE3D error under the KITTI data is only 0.076 m, which proves the effectiveness of the method.