Visual Research Articles

Time course of visual attention in rats by atomic magnetometer.

Atomic magnetometer (AM) is utilized to non-invasively detect event-related magnetic fields (ERMFs) evoked by visual stimuli in rats. The aim of this study was to investigate the relationship between N2-like amplitude and visual attention. To achieve this, we combined the AM with a visual stimulation system and employed the passive single-stimulus paradigm. By measuring the ERMFs at various inter-stimulus intervals (ISIs) with a sensitivity of 20 fT/[Formula: see text], we analyzed the effects of the ISI and the 'habituation' resulting from repeated stimuli on the N2-like amplitude. Our method serves as a valuable reference for studying the passive single-stimulus paradigm and the time course of mammalian attention.

Enhanced Edge Detection through Binary Particle Swarm Optimization and L0 Guided Filtering

Detecting edges holds significant importance in image processing, serving as a fundamental step in numerous computer vision applications. This paper presents an innovative method for performing edge detection by combining Binary Particle Swarm Optimization (BPSO) with L0 Guided Filtering. The proposed method aims to address the challenge of accurately detecting edges in noisy and complex images by leveraging the benefits of both BPSO and L0 guided filtering. The process begins with the initialization of the BPSO algorithm, where binary particles traverse the solution space to optimize parameters critical for edge detection. These optimized parameters are subsequently employed in the L0 guided filtering framework, a sophisticated edge preserving filter known for its ability to maintain fine details while effectively reducing noise. The synergy of BPSO and L0 guided filtering demonstrates improved adaptability to diverse image characteristics, enhancing the overall robustness of edge detection. The binary nature of BPSO allows for efficient exploration of the solution space, facilitating faster convergence to optimal parameters. Concurrently, the L0 guided filtering ensures edge preserving smoothing, contributing to the suppression of unwanted artifacts. Experimental evaluations on benchmark datasets showcase the effectiveness of the proposed method compared to traditional edge detection techniques. The results indicate superior edge localization and reduced sensitivity to noise, highlighting the potential of the BPSO Based Edge Detection under L0 Guided Filtering in real world applications. The presented approach offers a valuable contribution to the advancement of edge detection methodologies, demonstrating its potential for enhancing the performance of computer vision systems in various domains.

Machine vision system for automatic defect detection of ultrasound probes

Industry 4.0 conceptualizes the automation of processes through the introduction of technologies such as artificial intelligence and advanced robotics, resulting in a significant production improvement. Detecting defects in the production process, predicting mechanical malfunctions in the assembly line, and identifying defects of the final product are just a few examples of applications of these technologies. In this context, this work focuses on the detection of ultrasound probes’ surface defects, with a focus on Esaote S.p.A.’s production line probes. To date, this control is performed manually and therefore biased by many factors such as surface morphology, color, size of the defect, and by lighting conditions (which can cause reflections preventing detection). To overcome these shortfalls, this work proposes a fully automatic machine vision system for surface acquisition of ultrasound probes coupled with an automated defect detection system that leverage artificial intelligence. The paper addresses two crucial steps: (i) the development of the acquisition system (i.e., selection of the acquisition device, analysis of the illumination system, and design of the camera handling system); (ii) the analysis of neural network models for defect detection and classification by comparing three possible solutions (i.e., MMSD-Net, ResNet, EfficientNet). The results suggest that the developed system has the potential to be used as a defect detection tool in the production line (full image acquisition cycle takes ~ 200 s), with the best detection accuracy obtained with the EfficientNet model being 98.63% and a classification accuracy of 81.90%.

The Photometric Testing of High-Resolution Digital Cameras from Smartphones-A Pilot Study.

Luminance is the fundamental photometric quantity representing the technical meaning of brightness. It is usually measured from a distance using a matrix sensor, which is the basis of the professional instrument. However, specific technical requirements must be fulfilled to achieve accurate results. This paper considers whether modern high-resolution smartphone cameras are suitable for luminance measurements. Three cameras from high-end smartphones were evaluated on a dedicated laboratory stand. The sensors' output characteristics showed relatively good linearity of the individual R, G, and B channels. Unfortunately, the spectral sensitivities were unfavorable, as the minimum error achieved was about 17%. This device is classified outside the generally accepted quality scale for photometric instruments. The presented investigation confirmed that none of the high-resolution smartphone cameras tested was suitable for use as a universal luminance camera. However, one of the test devices can be developmental if restrictively calibrated and used only in a specialistic laboratory stand. Using a smartphone (or only its camera) for luminance measurements requires proper advanced calibration. It is possible, but it limits us to only dedicated applications. The pilot study presented in this paper will help create a suitable test stand for spectacle vision systems, e.g., virtual reality equipment.

A novel spatial pyramid-enhanced indoor visual positioning method

As a key application of Internet of Things (IoT) technology, visual localization plays an important role in everyday life. However, pedestrians in images can obstruct environmental features, negatively impacting the performance of visual localization systems. To address this issue, we propose a Spatial Pyramid-Enhanced MixVPR visual localization method (SPE-VL) that aims to enhance image feature descriptions through multi-scale spatial information, thereby mitigating the effects of pedestrian occlusion on localization accuracy. The SPE-VL method is divided into two main phases: sensor-based matching range constraint and image feature extraction and matching. In the matching range constraint phase, we propose a direction decision method based on a machine learning classifier that utilizes smartphone sensor data to restrict the direction of image matching, reducing the likelihood of mismatches. In the feature extraction and matching phase, we propose a Transformer-based feature cross-enhancement method that leverages local features and spatial contextual information to enhance features, improving both image retrieval accuracy and localization precision. Experimental results indicate that the SPE-VL method demonstrates higher localization accuracy and robustness compared to existing state-of-the-art methods, providing new insights and solutions for the application of visual localization in complex environments.

Validation of two simple scores to identify increased risk of transthyretin amyloid cardiomyopathy in heart failure with preserved ejection fraction

Abstract Background Transthyretin Amyloid Cardiomyopathy (ATTR-CM) is an emerging underdiagnosed cause of heart failure (HF). 99mTc-3, 3-diphosphono-1,2-propanodicarboxylic acid (DPD) allows ATTR-CM diagnosis in the absence of histology. Recently two simple clinical scores to identify increased risk of ATTR-CM in patients with heart failure and preserved left ventricular ejection fraction have been developed and validated. Purpose To assess the prevalence of ATTR-CM in a Mediterranean cohort of patients at risk, and to externally validate the Mayo ATTR-CM and T-Amylo scores. We also explore its diagnostic accuracy improvement by adding N-terminal pro-B-type natriuretic peptide (NT-proBNP). Methods Retrospective cohort study of consecutive patients with suspected cardiac amyloidosis referred to DPD between March 2017 and November 2020. Scans were reported using a visual grading system (0 to 3 increasingly; 2-3 positive) on planar images. Patients with clonal dyscrasia or those without rule-out tests (serum free light chain assay, and serum and urine protein electrophoresis with immunofixation) were excluded. Discrimination was obtained using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve based on the ATTR-CM score [High risk: score ≥ 6. Components: age (60-69 = 2 points; 70-79 = 3 points; ≥80 = 4 points), male sex = 2 points, hypertension = -1 point, ejection fraction <60% = 1 point, posterior wall thickness ≥12 mm = 1 point, relative wall thickness (septal + posterior wall thickness/left ventricular end diastolic diameter) >0.57 = 2 points], and T-amylo score [High risk: score > 6. Components: age ≥ 80 years = 1 point, interventricular septum thickness ≥16 mm = 2 points, low QRS interval voltage = 2 points, male gender = 3 points, and carpal tunnel syndrome = 3 points. Calibration was assessed with the Hosmer-Lemeshow goodness-of-fit test. Results A total of 200 patients were included with a mean age of 78.5 ± 11 years, 45.5% male, left ventricular ejection fraction 58% ± 12 and a median NT-proBNP of 2310 pg/mL (Table 1). 81 (40.5%) and 10 (5.0%) patients were categorized as a high-risk group due to ATTR-CM score value ≥6 and simplified T-Amylo score >6, respectively. Prevalence of ATTR-CM was 17.5%. The AUC for the Mayo ATTR-CM and T-Amylo scores in our cohort were 0.83 (95% CI 0.75-0.90) and 0.79 (95% CI 0.69-0.88) respectively, with an appropriated calibration (Figure 1). The addition of NT-proBNP to the scores did not increase the AUC (p=0.19 and p=0.08), with a Net Reclassification Improvement (NRI) of 0.055 (95% CI - 0.110 to 0.206) and 0.033 (95% CI - 0.113 to 0.224) respectively. Conclusion These two simple clinical scores are valid and reliable tools for ATTR-CM diagnosis in our Mediterranean cohort. Added to clinical suspicion, can improve the diagnostic workup for ATTR-CM, irrespective of NT-proBNP values.

Bidirectional Photovoltage-Driven Oxide Transistors for Neuromorphic Visual Sensors.

Biological vision is one of the most important parts of the human perception system. However, emulating biological visuals is challenging because it requires complementary photoexcitation and photoinhibition. Here, the study presents a bidirectional photovoltage-driven neuromorphic visual sensor (BPNVS) that is constructed by monolithically integrating two perovskite solar cells (PSCs) with dual-gate ion-gel-gated oxide transistors. PSCs act as photoreceptors, converting external visual stimuli into electrical signals, whereas oxide transistors generate neuromorphic signal outputs that can be adjusted to produce positive and negative photoresponses. This device mimics the human vision system's ability to recognize colored and color-mixed patterns. The device achieves a static color recognition accuracy of 96% by utilizing the reservoir computing system for feature extraction. The BPNVS mem-reservoir chip is also proposed for handing object movement and dynamic color recognition. This work is a significant step forward in neuromorphic sensing and complex pattern recognition.

Motion Recognition Based on Deep Learning Algorithm

Advanced scientific and technological measurement methods are the basis of sports research and the progress of sports research methods. It promotes the continuous improvement of sports technology to meet athletes’ pursuit of excellent sports achievements. Therefore, the method of sports scientific research must be changed from the traditional empirical training mode to the programmed training method, from the qualitative analysis of the training effect to the nuanced analysis of the training process. Based on this, this paper uses in-depth learning to study sports motion capture. First, two-dimensional human joints are extracted based on Mask R-CNN. Then, the 3D human motion skeleton is constructed by using the binocular vision system, and the Mask R-CNN human pose estimation algorithm is optimized. On this basis, a sports motion capture system is designed, and the system’s accuracy is verified. The error of the depth information obtained by the sports capture system constructed in this paper is less than 3% in the experiment of about 2[Formula: see text]m. It has strong practicability.

Anatomical Connectivity Constrains Dynamic Functional Connectivity among Neural Systems: Implications for Cognition and Behavior

Abstract Leslie Ungerleider had a tremendous impact across many different areas of cognitive neuroscience. Her ideas and her approach, as well as her findings, will continue to impact the field for generations to come. One of the most impactful aspects of her approach was her focus on the ways that anatomical connections constrain functional communications among brain regions. Furthermore, she emphasized that changes in these functional communications, whether from lesions to the anatomical connections or temporary modulations of the efficacy of information transmission resulting from selective attention, have consequences for cognition and behavior. By necessity, this short review cannot cover the vast amount of research that contributed to or benefited from Leslie's work. Rather, we focus on one line of research that grew directly from some of Leslie's early work and her mentoring on these important concepts. This research and the many other lines of research that arose from these same origins has helped develop our understanding of the visual system, and cognitive systems more generally, as collections of highly organized, specialized, dynamic, and interacting subsystems.

Insect Phototaxis Mechanisms Innovations in Pest Control Strategies and Applications

Phototaxis, the movement of insects toward or away from light, is a critical behavioral response that influences feeding, mating, and habitat selection. Understanding the mechanisms behind insect phototaxis can lead to the development of effective and environmentally sustainable pest control strategies and explores the biological foundations of phototaxis, including the role of insect visual systems, light sensitivity, and circadian rhythms. Recent advancements in phototaxis-based pest management, particularly the use of energy-efficient LED light traps, solar-powered traps, and wavelength-specific attractants, are also discussed. These innovations offer promising alternatives to chemical pesticides, reducing environmental impact and enhancing agricultural sustainability. The integration of phototaxis-based tools with other methods, such as biological control and habitat management, is highlighted as a way to improve pest suppression while preserving beneficial insect species. Challenges, including non-target effects, variability in insect responses, and potential light pollution, are also addressed. To emphasize the need for tailored light wavelengths, smart farming integration, and eco-friendly solutions that minimize unintended ecological consequences and underscores the potential of insect phototaxis to transform pest management, offering a comprehensive framework for more sustainable agricultural practices.

A new role for excitation in the retinal direction-selective circuit.

A key feature of the receptive field of neurons in the visual system is their centre-surround antagonism, whereby the centre and the surround exhibit responses of opposite polarity. This organization is thought to enhance visual acuity, but whether and how such antagonism plays a role in more complex processing remains poorly understood. Here, we investigate the role of centre and surround receptive fields in retinal direction selectivity by exposing posterior-preferring On-Off direction-selective ganglion cells (pDSGCs) to adaptive light and recording their response to globally moving objects. We reveal that light adaptation leads to surround expansion in pDSGCs. The pDSGCs maintain their original directional tuning in the centre receptive field, but present the oppositely tuned response in their surround. Notably, although inhibition is the main substrate for retinal direction selectivity, we found that following light adaptation, both the centre- and surround-mediated responses originate from directionally tuned excitatory inputs. Multi-electrode array recordings show similar oppositely tuned responses in other DSGC subtypes. Together, these data attribute a new role for excitation in the direction-selective circuit. This excitation carries an antagonistic centre-surround property, possibly designed to sharpen the detection of motion direction in the retina. KEY POINTS: Receptive fields of direction-selective retinal ganglion cells expand asymmetrically following light adaptation. The increase in the surround receptive field generates a delayed spiking phase that is tuned to the null direction and is mediated by excitation. Following light adaptation, excitation rules the computation in the centre receptive field and is tuned to the preferred direction. GABAergic and glycinergic inputs modulate the null-tuned delayed response differentially. Null-tuned delayed spiking phases can be detected in all types of direction-selective retinal ganglion cells. Light adaptation exposes a hidden directional excitation in the circuit, which is tuned to opposite directions in the centre and surround receptive fields.

Early visual modulation and selection predict saccadic timing during visual search: An ERP study.

Saccadic eye movements, a critical aspect of real-world visual behavior, are preceded by an initial accumulation of visual information followed by the selection of a single location to move one's eyes. However, it is currently unclear how each of these stages uniquely affects saccadic timing. In this study, participants searched for a contour integration target while EEG was used to measure posterior cortical activity between search display onset and first saccade initiation. The goal was to determine whether saccade timing could be attributed to differences in early ERP amplitudes, with the P1 reflecting the magnitude of early perceptual information accumulation and the N1 reflecting the strength of selection leading to the saccadic decision. EOG was used to measure saccade timing, and trials were divided into fast, middle, and slow bins. The N1 response was smallest in the slow saccade tertile, relative to both the fast and middle tertiles, suggesting weak selection. In contrast, the P1 response was largest for this same slow saccadic tertile relative to the middle saccadic tertile, suggesting vigorous information accumulation. Therefore, delays in saccadic behavior may occur when the visual system is overwhelmed with visual input, thus increasing the time to reach a saccadic decision. These findings reconcile models of eye movement behavior which often prioritize either the impact of information accrual or selection, rather than regarding both as an integrated whole.

Effect of attention on ensemble perception: Comparison between exogenous attention, endogenous attention, and depth.

Ensemble perception is an important ability of human beings that allows one to extract summary information for scenes and environments that contain information that far exceeds the processing limit of the visual system. Although attention has been shown to bias ensemble perception, two important questions remain unclear: (1) whether direct manipulations on different types of spatial attention could produce similar effects on ensembles and (2) whether factors potentially influencing the attention distribution, such as depth perception, could evoke an indirect effect of attention on ensemble representation. This study aims to address these questions. In Experiments 1 and 2, two types of precues were used to evoke exogenous and endogenous attention, respectively, and the ensemble color perceptions were examined. We found that both exogenous and endogenous attention biased ensemble representation towards the attended items, and the latter produced a greater effect. In Experiments 3 and 4, we examined whether depth perception could affect color ensembles by indirectly influencing attention allocation in 3D space. The items were separated in two depth planes, and no explicit cues were applied. The results showed that color ensemble was biased to closer items when depth information was task relevant. This suggests that ensemble perception is naturally biased in 3D space, probably through the mechanism of attention. Computational modeling consistently showed that attention exerted a direct shift on the ensemble statistics rather than averaging the feature values over the cued and noncued items, providing evidence against an averaging process of individual perception.

A preliminary exploration of quantitative models for evaluating binocular visual perception impairments among patients diagnosed with stroke.

The objective of this study is to formulate and implement graded biological models pertaining to binocular visual perception function with the use of computer algorithms. We aim to quantitatively assess the location, severity, and degree of impairment in binocular visual perception among patients who have suffered stroke, thereby providing valuable insights into the repercussions of cerebral tissue damage on the visual system. To overcome the shortcomings of previous instruments used to assess binocular function in terms of stereoscopic effects and the challenges posed by physiological and psychological interference during examinations, this study optimized its approach by integrating polarized stereovision technology with computer graphic modeling techniques. This study employed computer models to assess binocular visual perception function in stroke patients. Computer models refer to psychophysical testing methods used to measure binocular visual perception function, including various assessment tasks such as recognizing inverted letters and assessing stereopsis during high-speed movements. The cross-into-circle test was used as a means to quantify perceptual eye position. Subsequently, the collected data was analyzed to assess the magnitude of impairment in binocular visual perception. The results of the study revealed a spectrum of binocular visual perception impairment among patients diagnosed with stroke, demonstrating discernible variations in the recognition of inverted letters and stereopsis across different movement speeds. Importantly, perceptual eye position measurements offered valuable insights into ocular misalignment. The computational models effectively quantified both the spatial distribution and severity of these identified impairments. Damage to brain tissue resulting from a stroke can give rise to notable impairments in binocular visual perception function. Graded biological models, formulated through computer algorithms, provide a systematic framework for the comprehensive evaluation and quantification of these impairments. The comprehension of the nature and extent of visual impairments observed in patients with stroke establishes a basis for the development of personalized visual perception learning methodologies. Based on such tailored approaches, we aim to facilitate the recovery of impaired visual function, thereby contributing to the broader objective of neural system rehabilitation.

When sounds come alive: animacy in the auditory sense.

Despite the interest in animacy perception, few studies have considered sensory modalities other than vision. However, even everyday experience suggests that the auditory sense can also contribute to the recognition of animate beings, for example through the identification of voice-like sounds or through the perception of sounds that are the by-products of locomotion. Here we review the studies that have investigated the responses of humans and other animals to different acoustic features that may indicate the presence of a living entity, with particular attention to the neurophysiological mechanisms underlying such perception. Specifically, we have identified three different auditory animacy cues in the existing literature, namely voicelikeness, consonance, and acoustic motion. While the first two characteristics are clearly exclusive to the auditory sense and indicate the presence of an animate being capable of producing vocalizations or harmonic sounds-with the adaptive value of consonance also being exploited in musical compositions in which the musician wants to convey certain meanings-acoustic movement is, on the other hand, closely linked to the perception of animacy in the visual sense, in particular to self-propelled and biological motion stimuli. The results presented here support the existence of a multifaceted auditory sense of animacy that is shared by different distantly related species and probably represents an innate predisposition, and also suggest that the mechanisms underlying the perception of living things may all be part of an integrated network involving different sensory modalities.

ViCo: Human visual perception-based contour extraction of Chinese Tang dynasty textile patterns

The study of ancient textile patterns not only enriches historical material but also inspires modern costume art design. Recent developments in computer vision technology provide novel analytical methods for the study of ancient textile patterns. As contours are one of the most significant features in computer vision tasks, contour extraction serves as a prerequisite to accomplishing intelligent understanding and automatic design of textile patterns. In this paper, a perception-consistent contour extraction method (ViCo) that simulates the human visual system from the retina, the lateral geniculate nucleus (LGN), to the V1 area in the cortex is proposed, along with an ancient textile pattern dataset represented by TuanKe in the Chinese Tang dynasty. Experiments were conducted both qualitatively and quantitatively to verify the effectiveness of the proposed method on the TuanKe dataset. Analysis of the results implies the effectiveness of ViCo because the retinal and LGN filter enhances edge information and restrains tiny pixel changes, and the cortex simulation is direction selective. This study is among the initial efforts to employ computer vision technology for ancient textile pattern studies. The study presents a TuanKe dataset and a novel bio-inspired contour extraction method, which has significant historical research importance, fabric art design inspiration, and computer application value.

Determining the effectiveness of using three-dimensional printing to train computer vision systems for landmine detection

The object of this study is the effectiveness of using three-dimensional printing to train computer vision models for landmine detection. The ongoing war in Ukraine has resulted in significant landmine contamination, particularly after russia’s full-scale invasion in 2022. Given the enormous amount of potentially landmine-contaminated land, fast and efficient demining techniques are required, as human probing and metal detectors are labor-intensive and slow-moving. Machine learning offers promising solutions to speed up the landmine detection process by deploying recognition models on robots and unmanned aerial vehicles. However, training such systems faces certain challenges. Firstly, the number of annotated data available for training is limited, which can hinder the model’s ability to generalize to real-world scenarios. Secondly, the use of real or even defused landmines is dangerous due to the potential for accidental detonation. This study aims to overcome the problem of limited data and the risk of using real landmines. Three-dimensional printing makes it possible to create safe and diverse training data, which is essential for model performance. The model trained on replicas, achieved 98 % and 91 % precision on printed and actual landmines, respectively. This high precision is attributed to the realism of copies and the use of advanced machine learning algorithms. This approach successfully addressed the research problem due to the safety, accessibility and diversity of copies. The models trained on copies of landmines could be used in humanitarian demining operations. These operations often employ unmanned aerial vehicles or robots to identify landmines that are thrown remotely, exposed on the surface, or partially hidden

A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected.

Теорія фантомізації радіозображень РСА: базові відомості та обґрунтування розмірів ділянки фантомізації

The article's subject of study is the creation of the foundations of the theory of the formation of false elements in the images of aerospace radio vision systems by injecting informational signs of objects absent on the underlying surface. The goal is to substantiate the dimensions of the subsurface areas on which it is possible to simulate objects with different methods of viewing the exploring area from the aerospace radar carrier. Task: to develop the geometry of the problem, which will reflect the interaction between the radar with the synthesis of the Synthetic-aperture radar (SAR) and the radar of phantomization (FR); to develop a general structural diagram of the FR; determine the main parameters, taking into account which will allow planning an "interaction" session between the SAR and the FR; to determine the sizes of the underlying surface areas, where it is possible to provide phantomization of radio images formed by SAR with different methods of examination. The methods used are mathematical and functional analysis, navigation, and remote sensing. The following results were obtained. The geometry of the problem of the interaction between the SAR and the FR was defined; it allowed us to calculate the interaction time between the systems and to establish the law of change of the distance between them at any placement of the SAR in orbit. The influence of the interposition of the considered systems on the expected shape of the SAR sounding signal and the size of the area on which the problem of radio image phantomization arises has been investigated. Examples of calculations of the specified parameters for various types of exploring of the earth's surface by radar from an aerospace carrier are given. Conclusions. The article summarizes and develops the theory of phantom radio images constructed using SAR. The obtained results establishing the dependence between the phantomization geometry and such parameters as the potential and actual time of interaction between the systems, the envelope of the bundle of received radio pulses, and the number of separation bands by range are important for further research in this direction, which at the moment it is advisable to focus on solving the optimization problem phantomization of separate blocks of the radar and determining the conditions for planning the processes of injecting objects into the transmitter signal and into the radio image formed by the SAR.

LIVS: a tightly coupled LiDAR, IMU, and camera-based SLAM system under scene degradation

In increasingly complex environments, single-sensor-based SLAM systems face scene degradation problems such as strong light, dynamic obstacle interference, and lack of structural features in the environment, which severely constrain the localization accuracy and mapping effectiveness of SLAM systems. To address this problem, this paper proposes a tightly coupled laser-IMU-visual SLAM system (LIVS) based on factor graph optimization. LIVS consists of two subsystems: the laser inertial system (LIS) and the visual inertial system (VIS). The pose estimation of the LIS can provide an initialization for the VIS; LiDAR suffers scene degradation in environments lacking structural features, while the VIS provides better constraints for Lidar in this environment. Firstly, a method of selecting keyframes based on ORB features and the relative motion of the mobile robot is proposed as a means of improving the positioning accuracy and tracking ability of the system in the case of strenuous motion, and a strategy of local sliding window optimization is adopted to effectively improve the real-time performance and robustness of the system. Meanwhile, in order to further improve the global map consistency and positioning accuracy, a hybrid closed-loop detection method based on laser and vision is proposed, which utilizes the geometrical features of laser and the image features of vision to accomplish the hybrid closed-loop detection. Secondly, the laser inertia factor, IMU pre-integration factor, vision inertia factor, and closed-loop factor are globally optimized in a factor map framework to achieve accurate estimation of the robot pose. Finally, this paper presents experimental evaluations on both public and homemade datasets and compares the performance of the proposed algorithm with that of other algorithms. Experimental results show that the algorithm in this paper exhibits better performance in both localization accuracy and mapping effectiveness.