Visual Mapping Research Articles

LL-VI SLAM: enhanced visual-inertial SLAM for low-light environments

Abstract In low-light environments, the scarcity of visual information makes feature extraction and matching challenging for traditional visual simultaneous localization and mapping (SLAM) systems. Changes in ambient lighting can also reduce the accuracy and recall of loop closure detection. Most existing image enhancement methods tend to introduce noise, artifacts, and color distortions when enhancing images. To address these issues, we propose an innovative low-light visual-inertial (LL-VI) SLAM system, named LL-VI SLAM, which integrates an image enhancement network into the front end of the SLAM system. This system consists of a learning-based low-light enhancement network and an improved visual-inertial odometry. Our low-light enhancement network, composed of a Retinex-based enhancer and a U-Net-based denoiser, enhances image brightness while mitigating the adverse effects of noise and artifacts. Additionally, we incorporate a robust Inertial Measurement Unit initialization process at the front end of the system to accurately estimate gyroscope biases and improve rotational estimation accuracy. Experimental results demonstrate that LL-VI SLAM outperforms existing methods on three datasets, namely LOLv1, ETH3D, and TUM VI, as well as in real-world scenarios. Our approach achieves a peak signal-to-noise ratio of 22.08 dB. Moreover, on the TUM VI dataset, our system reduces localization error by 22.05% compared to ORB-SLAM3, proving the accuracy and robustness of the proposed method in low-light environments.

EphA4 mediates ephrinB1-dependent adhesion in retinal ganglion cells

Eph/ephrin signaling is crucial for organizing retinotopic maps in vertebrates. Unlike other EphAs, which are expressed in the embryonic ventral retina, EphA4 is found in the retinal ganglion cell (RGC) layer at perinatal stages, and its role in mammalian visual system development remains unclear. Using classicin vitrostripe assays, we demonstrate that, while RGC axons are repelled by ephrinB2, they grow on ephrinB1 stripes through EphA4-mediated adhesion. In vivo, retinal axons from EphA4-deficient mice from either sex show impaired arborization in the medial, but not lateral, regions of the superior colliculus that express ephrinB1. Gain-of-function experiments further reveal that ephrinB1-mediated adhesion depends on EphA4 tyrosine kinase activity but it is independent of its Sterile Alpha Motif. Together, our findings suggest that EphA4/ephrinB1 forward signaling likely facilitates adhesion between retinal axon terminals and cells in the medial colliculus, contributing to the establishment of proper connectivity within the visual system.Significance statementThe significance of our findings goes beyond unveiling the mechanisms underlying topographic visual map formation. By discovering a dual role for EphA4 in mediating both repulsion and adhesion, which challenges the conventional understanding of EphA4/ephrin interactions, this work opens up new avenues for exploring EphA4 broader implications in other cellular contexts, including cell differentiation, migration and synaptic plasticity.

Ground Robots Bring Potential for Efficiency, Safety in FPSO Operations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32872, “Evaluation of the Potential Impact of Ground Robots on FPSO Operations,” by Gustavo L.L. da Silva, Maurício M. Cordeiro Jr., and Maurício Galassi, Petrobras, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ The objective of this paper is to present the operator’s learnings in evaluating the processes by which the asset crew of a floating production, storage, and offloading (FPSO) vessel identifies scenarios for the application of robotics in day-to-day offshore activities. The studies reveal a glimpse of the addressable market size of ground robots for routine offshore verification tasks. An initial analysis suggests that 30–50% of the theoretical equivalent of hours per year could be freed. Introduction In the complete paper, the hypothetical use of ground mobile robots to carry out operational activities is considered. For that goal, the capability of robotic platforms to move along an offshore facility (an FPSO, for the purposes of this paper) has been explored. This includes the ability to overcome obstacles, move up and down stairs or ramps, and walk on uneven floors or gratings while being piloted remotely or even doing so autonomously. Some of the considered equipment may have coupled robotic manipulators that also enable them to interact with the environment, allowing them to open or close doors, turn handles, and activate commands. These robotic platforms, in general, include actuator mechanisms and sensors that enable visual inspections, thermography, dimensional analysis, area mapping, gas measurements, noise and vibration analysis, and other operations requiring specific sensing. Operational Requirements A paramount requirement for a robot to be widely used in an FPSO is its ability to operate in hazardous areas. Robotic platforms for hazardous zones are not widely available. However, some companies are quickly advancing in the development and commercialization of these platforms. A Swiss company has already made available a quadruped robot with high mobilization capacity, including the ability to navigate stairs and carry out autonomous missions that have been preprogrammed. An Austrian company has marketed a caterpillar robot that can carry out inspections autonomously in classified areas. Every such activity will demand thorough analysis to properly define technical and process requirements. On FPSO platforms, the procedure for planning any service takes place at least a few days before its execution, a task already demanding many hours. Subsequently, a permit to work (PTW) is issued to perform the service. Sensors and Actuators for a Robotic System Some of the main data-acquisition or processing sensors used in robotic systems are detailed in the complete paper. In this context, “data” means everything that can be processed online or is capable of producing useful information in the future. Cameras for Acquisition of Images and Videos. These can be very useful in the visual inspection of systems or equipment, the visualization of the workspace, or the generation of input for image processing with the purpose of identifying patterns of interest, segmentation, classification, or other aspects related to computer vision.

Bactogram: Spatial Analysis of Bacterial Colonisation in Epidermal Wounds.

Skin barrier damage and subsequent development of harmful microbiota contribute to conditions such as wound infections, atopic dermatitis and chronic wounds, which impact millions of people globally and pose a significant economic burden on healthcare systems. Established microbial sampling methods, such as swabs and tissue biopsies, provide limited information on the spatial distribution of bacteria. We here describe a new method that produces a visual map of the distribution of cultivable bacteria, denoted 'Bactogram', across the whole wound and surrounding skin, suitable for image-based quantification. As part of an exploratory endpoint in a clinical trial we applied the Bactogram method to 48 suction blister wounds in 24 healthy volunteers. Bacteria developed in all wounds, predominantly on the skin under the dressing and near wound edges. Two quantification methods, based on visual scoring and image analysis, demonstrated high inter-, and intra-rater agreement and were used to characterise bacterial re-colonisation during epidermal wound healing. We also demonstrated proof of concept that the method can be used with chromogenic agar to enable spatial identification of pathogenic bacterial species, such as Staphylococcus aureus. In conclusion, this study introduces a simple method for sampling bacteria over large areas and generating a bacterial map that can identify spatial variations in bacterial composition and abundance in skin and wound conditions. Trial Registration: ClinicalTrials.gov identifier: NCT05378997.

Monocular thermal SLAM with neural radiance fields for 3D scene reconstruction

Visual simultaneous localization and mapping (SLAM) faces significant challenges in environments with variable lighting and smoke. Excelling in such visually degraded settings, thermal imaging captures scene radiance effectively. To address the limitations of traditional thermal SLAM in 3D scene reconstruction, we propose ThermalSLAM-NeRF, a novel integration of thermal SLAM with neural radiance fields (NeRF). This method significantly enhances the quality of high dynamic range thermal images by improving their signal-to-noise ratio, contrast, and detail. It also employs online photometric calibration to ensure grayscale consistency between consecutive frames. We utilize a sparse direct method for pose estimation, selecting keyframes based on photometric error and tracking quality. The NeRF map is reconstructed using a multi-view keyframe sequence. Our evaluations on datasets containing over 15,000 thermal images show that ThermalSLAM-NeRF achieves an average improvement of 59.30% in trajectory accuracy over existing state-of-the-art SLAM methods. This approach uniquely tracks all sequences and constructs comprehensive NeRF maps, enabling robust and precise pose estimation without the need for extensive pre-training.

Research trends and hotspots on septic shock: a bibliometric study and visualization analysis

BackgroundSeptic shock, the most severe stage of sepsis, causes potential circulatory failure and abnormal cell metabolism which are severe enough to affect prognosis, increase mortality, and impose significant burdens on the medical system. Despite a growing number of studies exploring the pathophysiology, epidemiology, and risk factors, research trends and hotspots in septic shock remain lacking. This study aims to create a visual knowledge map, identify research hotspots, and predict prospective trends based on bibliometric analysis.MethodsWe searched for publications related to septic shock in Web of Science Core Collection up to June 15, 2023. CiteSpace5.5 R2, VOS viewer and Pathfinder were used to evaluate the annual publications, countries, institutions, journals and keywords. We also analyzed the collaboration among countries, institutions and authors, and identified research hotspots and frontiers.ResultsA total of 4,208 English papers were included in the analysis, and the annual publication displayed a slow upward trend. In terms of publication volume, the top three countries were the United States, France, and Germany, and the University of Pittsburgh (the United States) ranked first (n = 85) among all institutions, with Jeanlouis Vincent from Erasmus Medical Center (Netherlands) as the most published author (n = 32). According to the collaborative network, the United States had the highest level of cooperation, and the University of Pittsburgh, the University of Toronto, and Columbia University were the institutions with the most foreign cooperation. Additionally, the co-author network revealed that scholars such as Jeanlouis Vincent, Rinaldo Bellomo, and Djillali Annane, had the strongest collaborations. The co-citation network showed that the top 3 most cited articles were: Singer M (2016), Rhodes A (2017), Dellinger RP (2013), and the top 3 most cited journals were Crit Care Med (3,664 times), N Engl J Med (3,207 times), Intens Care Med (3,096 times) in this field. In the keyword co-occurrence network, the most frequent keywords were “septic shock” (2531), “sepsis” (1667), and “mortality” (569), indicating the current research hotspots. Pathobiology, fluid therapy, and endotoxic septic shock were emerging trends in research.ConclusionBy using bibliometrics, this study reviewed the studies in septic shock and revealed the hotspots and cutting-edge trends, including the pathogenesis of complications, the development of new biomarkers, the timing and methods of alternative treatments, and the rehabilitation trajectory, etc., which provided a reference for subsequent studies in septic shock.

Spatial analysis of anemia prevalence in early childhood in Northeastern Thailand, 2020–2022

ABSTRACT Anemia poses a significant health challenge for Thai children, yet data on its geographic distribution among those aged 6–12 months is limited. This study investigates spatial variations in anemia prevalence across northeastern Thailand using 2020–2022 national data. Global and local Moran’s I analyses identified significant, nonrandom patterns, with hotspots in central and northeastern regions and coldspots in the southeast. Visualization maps highlight provincial variations, emphasizing the need for targeted interventions. These results provide critical insights for policymakers to allocate health resources effectively and develop strategic anemia intervention programs.

A Review of Multi-Sensor Fusion Techniques for Indoor Mobile Robot Navigation

The application of multi-sensor fusion technology in indoor mobile robot navigation and localization has been increasingly gaining attention, especially in complex indoor environments where achieving high-precision autonomous navigation poses a significant challenge. This review summarizes various sensor fusion methods, including the combination of Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU), Ultra-Wideband (UWB), and others, with a focus on discussing the progress of fusion algorithms such as Extended Kalman Filter (EKF) and Adaptive Monte Carlo Localization (AMCL) in improving navigation accuracy and stability. In addition, this paper explores the application of visual Simultaneous Localization and Mapping (SLAM) methods incorporating deep learning in indoor robot navigation. Finally, the main challenges currently faced by multi-sensor fusion technology in robot autonomous navigation are analyzed, and future research directions are proposed, aiming to provide valuable references for researchers in the field.

Classroom as Organization: Then, Now, and Next

Classroom as Organization (CAO) is a highly experiential teaching approach that transforms the classroom into a functioning, student-run organization. CAO emerged during the early days of the organizational behavior field and continues today. While CAO research has a rich history in the Journal of Management Education and its predecessors, this article marks the first comprehensive literature review on the topic. We review the CAO literature, trace the development of this methodology, and create a visual map of the key models. By exploring CAO’s past and present, the aim is to distill key insights about this method and outline avenues for more educators to adopt this innovative and empowering teaching methodology in the future. We conclude by calling for continued empirical research (at the class and program levels) and intentional community building to support faculty and encourage broader adoption of CAO.

Three-dimensional spectrum coverage gap map construction in cellular networks: A non-linear estimation approach

Data collection techniques can be used to determine the coverage conditions of a cellular communication network within a given area. In such tasks, the data acquisition process faces significant challenges for larger or inaccessible locations. Such challenges can be alleviated through the use of unmanned aerial vehicles (UAVs). This way, data acquisition obstacles can be overcome to acquire and process the necessary data points with relative ease to estimate a full area coverage map for the concerned network. In this study, we formulate the problem of deploying a UAV to acquire the minimum possible measurement data points in a geographical region for the purpose of constructing a full communication coverage gap map for this region. We then devise an estimation model that utilizes the measured data samples and determines the noise/loss levels of the communication links at the other unvisited spots of the region accordingly. The proposed estimation model is based on a cascade-forward neural network to allow for both nonlinear and direct linear relationships between the input data and the output estimations. We further investigate the conventional method of using linear regression estimators to decide on the received power levels at the different locations of the examined area. Our simulation evaluations show that the proposed nonlinear estimator outperforms the conventional linear regression technique in terms of the communication coverage error level while using the minimum possible collected data points. These minimum data points are then used in constructing a complete coverage gap map visualization that demonstrates the overall network service conditions within the surveyed region.

Improving ViT interpretability with patch-level mask prediction

Vision Transformers (ViTs) have demonstrated remarkable performances on various computer vision tasks. Attention scores are often used to explain the decision-making process of ViTs, showing which tokens are more important than others. However, the attention scores have several limitations as an explanation for ViT, such as conflicting with other explainable methods or highlighting unrelated tokens. In order to address this limitation, we propose a novel method for generating a visual explanation map from ViTs. Unlike previous approaches that rely on attention scores, our method leverages ViT features and conducts a single forward pass through our Patch-level Mask prediction (PM) module. Our visual explanation map provides class-dependent and probabilistic interpretation that can identify crucial regions of model decisions. Experimental results demonstrate that our approach outperforms previous techniques in both classification and interpretability aspects. Additionally, it can be applied to the weakly-supervised object localization (WSOL) tasks using pseudo mask labels. Our method requires no extra parameters and necessitates minimal locality supervision, utilizing less than 1% of the ImageNet-1k training dataset.

Accurate and Transferable Drug-Target Interaction Prediction with DrugLAMP.

Accurate prediction of drug-target interactions (DTIs), especially for novel targets or drugs, is crucial for accelerating drug discovery. Recent advances in pretrained language models (PLMs) and multi-modal learning present new opportunities to enhance DTI prediction by leveraging vast unlabeled molecular data and integrating complementary information from multiple modalities. We introduce DrugLAMP (PLM-Assisted Multi-modal Prediction), a PLM-based multi-modal framework for accurate and transferable DTI prediction. DrugLAMP integrates molecular graph and protein sequence features extracted by PLMs and traditional feature extractors. We introduce two novel multi-modal fusion modules: (1) Pocket-guided Co-Attention (PGCA), which uses protein pocket information to guide the attention mechanism on drug features, and (2) Paired Multi-modal Attention (PMMA), which enables effective cross-modal interactions between drug and protein features. These modules work together to enhance the model's ability to capture complex drug-protein interactions. Moreover, the Contrastive Compound-Protein Pre-training (2C2P) module enhances the model's generalization to real-world scenarios by aligning features across modalities and conditions. Comprehensive experiments demonstrate DrugLAMP's state-of-the-art performance on both standard benchmarks and challenging settings simulating real-world drug discovery, where test drugs/targets are unseen during training. Visualizations of attention maps and application to predict cryptic pockets and drug side effects further showcase DrugLAMP's strong interpretability and generalizability. Ablation studies confirm the contributions of the proposed modules. Source code and datasets are freely available at https://github.com/Lzcstan/DrugLAMP. All data originate from public sources. Supplementary data are available at Bioinformatics online.

PLM-SLAM: Enhanced Visual SLAM for Mobile Robots in Indoor Dynamic Scenes Leveraging Point-Line Features and Manhattan World Model

This paper proposes an enhanced visual simultaneous localization and mapping (vSLAM) algorithm tailored for mobile robots operating in indoor dynamic scenes. By incorporating point-line features and leveraging the Manhattan world model, the proposed PLM-SLAM framework significantly improves localization accuracy and map consistency. This algorithm optimizes the line features detected by the Line Segment Detector (LSD) through merging and pruning strategies, ensuring real-time performance. Subsequently, dynamic point-line features are rejected based on Lucas–Kanade (LK) optical flow, geometric constraints, and depth information, minimizing the impact of dynamic objects. The Manhattan world model is then utilized to reduce rotational estimation errors and optimize pose estimation. High-precision line feature matching and loop closure detection mechanisms further enhance the robustness and accuracy of the system. Experimental results demonstrate the superior performance of PLM-SLAM, particularly in high-dynamic indoor environments, outperforming existing state-of-the-art methods.

Synthesis, spectral, crystal structure, linear and NLO properties of quinoline Schiff base: Combined experimental and DFT calculations

The study explores the optical and nonlinear optical (NLO) properties of 2-imino [N- (N'-amido phenyl)] chromophore, a compound with potential in photonic applications. The compound was produced and crystallized using a slow evaporation approach using ethanol as solvent, and spectroscopy techniques were used to characterize it. The molecular structure of the compound was determined using FT-IR and NMR spectral methods. The crystal's structure was found via SCXRD analysis, showing a monoclinic system with a P21/c space group. The UV–Vis-NIR spectrum showed good transmittance over the visible region, with lower cutoff wavelengths of 338 nm and optical band gaps of 3.17 eV. The grown crystal exhibited a χ3 value of 2.89 × 10−8esu, making it valuable for non-linear optical applications. Quantum computational analysis was used to investigate molecular architectures, topologies, and molecule-level third-order NLO response. The compound exhibited a planar molecular geometry, with an average third-order NLO polarizability of 69.65 × 10−36 esu. The study also examined quantum chemically computed UV–visible spectra and electron density difference maps, as well as the density of states and molecular electrostatic potentials. In addition to highlighting the relationship between structure and characteristics, our current study of synthesized compounds highlights the potential of photonic applications.

Targeting ferroptosis reveals a new strategy for breast cancer treatment: a bibliometric study

BackgroundStudies exploring the role of ferroptosis in the pathogenesis of breast cancer have proliferated over the past decade, especially in 2023, with a staggering 217 publications in related studies. However, there are still significant gaps in comprehensive scientometric analysis and mapping of scientific studies, especially in terms of temporal and study area tracking, principal investigators, and the emergence of new hotspots.ObjectiveThis study aims to summarize the role of ferroptosis in the development of breast cancer and the latest research results on the ferroptosis-targeted treatment of breast cancer and to use bibliometric methods to draw a visual map to explore future research trends.MethodsOn May 11, 2024, this study updated the research progress related to ferroptosis and breast cancer over the past 11 years by retrieving data from January 1, 2014, to May 1, 2024, from the Web of Science database. In this research, many scientific analysis software including VOSviewer, chorddiag R Language Pack, Scimago Graphica, Citespace 6.3.R1, Cluster Profiler, enrichplot, ggplot2 R Language Pack, Cytoscape, and STRING online platform are used to make in-depth scientific analysis and visualization of the measurement results.ResultsStatistical analysis of these data showed that China accounted for 74.43% of the total publications, highlighting China’s dominant role in research on the relationship between ferroptosis and breast cancer. Several research institutions, including Sun Yat-sen University, Zhejiang University, and Shanghai Jiao Tong University, have achieved impressive results. Efferth, Thomas is the most prominent author in this field and has the highest number of publications in the subfield of oncology. This study clearly shows that ferroptosis plays a crucial role in the development of triple-negative breast cancer, hepatocellular carcinoma, glioma, leukemia, mitochondrial disease, lymphoma, bladder tumors, lung adenocarcinoma, and esophageal tumors.ConclusionThis study provides a comprehensive bibliometric evaluation that deepens our understanding of the role of ferroptosis in the pathogenesis of breast cancer and the current status of targeting ferroptosis for treating breast cancer. Thus, it helps researchers in related fields explore new research directions by comprehensively extracting important information and research hotspots.

Combining hyperspectral imaging technology and visible-near infrared spectroscopy with a data fusion strategy for the detection of soluble solids content in apples

Soluble solids content (SSC) is an important indicator for evaluating apple quality. This study aimed to assess two spectral techniques (hyperspectral imaging (HSI) and visible-near infrared (Vis-NIR)) combined with low-level and mid-level fusion strategies (LLF and MLF) for the detection of SSC in apples. Firstly, baseline correction (BC), detrending (DT), multiplicative scatter correction (MSC), savitzky-golay (SG), and standard normal variables (SNV) were used for the preprocessing of spectral data. Secondly, genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and their combinations were used for feature variable extraction. Finally, models were developed using partial least squares regression (PLSR) for spectral data. The results showed that Vis-NIR has an advantage over HSI in predicting apple SSC if only a single spectral technique was considered. The data fusion strategy showed better performance in predicting SSC metrics compared to individual spectral data. Among them, the LLF strategy showed the best performance in predicting SSC, with an Rp2 of 0.927, an RMSEP of 0.529 °Brix, and an Akike information criterion (AIC) of 332.96. In addition, an optimal model based on HSI data was used to achieve visual maps of SSC. It was demonstrated that the fusion of HSI and Vis-NIR data provided a promising method for detecting the SSC in apples.

Current Situation and Trend of the Research on the Consciousness of Community of the Chinese Nation: A Bibliometric Analysis Based on CSSCI Core Journals

To firmly build the sense of community of the Chinese nation is the main line of all work carried out in ethnic areas in the new era, and is an important ideological project of socialism with Chinese characteristics in the new era. This paper selected 1967 CSSCI papers on the consciousness of community of the Chinese nation in Cast-in-place from 2017 to 2024 as the research data, and used CiteSpace 6.3.R1 software to make statistics on the number of publications, author analysis, keyword co-occurrence analysis, keyword cluster analysis, keyword emergence analysis, and distribution analysis of publishing institutions and funds. Draw a visual visual map to show the research status and trend of the Chinese nation's community consciousness. The analysis results show that: the national policy has a strong guidance for building the Chinese nation's sense of community; Ethnic areas and ethnic colleges and universities have great influence on the research of the consciousness of the Chinese nation community. The research on the consciousness of the Chinese nation community involves a wide range of disciplines. The degree of researcher cooperation is low; In the current research, "times value", "common prosperity", "cultural self-confidence", "moral cultivation" and "symbols" have been extensively studied, which is the hot content of the current consciousness of the Chinese national community.

Strengthening Mental Health Though Resilience in Nursing Students: A Protocol for a Comprehensive Scoping Review.

Nursing students face unique challenges during their university education, making them vulnerable to mental health problems. Psychological resilience has been identified as a protective factor against these issues. However, previous reviews have identified gaps in the literature on resilience and mental health among nursing students. This scoping review aims to identify and map studies on psychological resilience and mental health in undergraduate nursing students, synthesize current evidence on their relationship, identify interventions for enhancing resilience, and highlight gaps in the existing literature. Studies published between January 2019 and April 2024 in English, Portuguese, and Spanish addressing resilience and mental health in undergraduate nursing students will be included. Primary studies, secondary studies, clinical guidelines, and grey literature will be considered. Searches will be conducted in multiple databases including EMBASE, ERIC, PubMed, Science Direct, Web of Science, DOAJ, ELSEVIER, EMERALD, and WILEY ONLINE LIBRARY. Grey literature sources will also be searched. Data will be extracted using a standardized form and synthesized narratively. Thematic analysis will be conducted using MAXQDA software ((Verbi GmbH, 24 version, 2023). Quantitative summaries, visual mapping, subgroup analyses, and trend analyses will be performed where appropriate. As this is a protocol, results are not yet available. The review will present a comprehensive map of the current literature on psychological resilience and mental health in nursing students, including identified interventions and research gaps. This scoping review will provide valuable insights to guide curriculum development, support services, and policy-making in nursing education. The findings may support actions to strengthen resilience and prevent mental health problems among future nursing professionals.

Visualization of Moisture Content, Reducing Sugars, and Chewiness in Bread During Oral Processing Based on Hyperspectral Imaging Technology.

This study evaluated the differences in oral processing and texture perception of breads with varying compositions. The research investigated the dynamic changes in moisture content (MC), reducing sugars (RSs), and chewiness of the bolus formed from white bread (B0) and 50% whole-wheat bread (B50) during oral processing. Hyperspectral imaging (HSI) combined with chemometric methods was used to establish quantitative prediction models for MC, RSs, and chewiness, and to create visual distribution maps of these parameters. The results showed that B0 had a higher moisture content and a faster hydration rate than B50 during the initial stages of oral processing, indicating greater hydrophilicity and ease of saliva wetting. Additionally, the uniformity of moisture distribution in the bolus of B0 was higher than that of B50. B50 exhibited significantly lower RSs content and poorer distribution uniformity compared to B0. The primary differences in chewiness between the two types of bread were observed during the early stages of oral processing, with B50 requiring more chewing effort initially. This study demonstrated that HSI technology can effectively monitor and elucidate the compositional changes in food particles during oral processing, providing new insights into bread texture perception and offering a scientific basis for improving bread processing and texture.

Multi-Sensor SLAM Positioning and Navigation

This paper provides a comprehensive review of advancements in Positioning and Navigation, focusing on the integration of key technologies to enhance operational efficiency and sustainability. The study examines the application of sensors in robotic systems, with particular emphasis on visual Simultaneous Localization and Mapping (SLAM) and LiDAR SLAM technologies. Visual SLAM, which uses monocular or binocular cameras to capture environmental images for navigation and mapping, offers cost-effective solutions but faces challenges such as sensitivity to lighting conditions and high computational demands. LiDAR SLAM, utilizing laser pulses to create precise 3D maps, complements visual SLAM by improving accuracy and robustness. The review also covers the integration of these technologies with deep learning and other advanced algorithms, highlighting their potential to enhance robotic performance and address practical limitations. The paper identifies future research directions aimed at advancing these technologies for more effective and sustainable practices.