Search Results (4,775)

Autonomous driving systems (ADSs) are reliable only when heterogeneous sensors, estimation algorithms, and safety mechanisms are engineered as a single coherent safety-critical measurement system rather than as loosely coupled modules. Production stacks integrate cameras, LiDAR, automotive radar, and GNSS/IMU, yet deployment remains constrained by modality-specific failure modes, calibration and synchronization drift, and out-of-distribution (OOD) conditions that violate modeling assumptions. These limitations induce overconfidence and downstream decision errors whenever planning assumes certainty sharper than sensing can justify. This survey introduces a sensor-centric framework linking measurement physics, uncertainty propagation, fusion integrity, safety assurance, and risk-aware planning and control. We formalize what each modality physically measures; unify probabilistic, evidential, and conformal uncertainty representations; analyze filtering, factor-graph, BEV, transformer, and state-space fusion architectures with an emphasis on robustness and graceful degradation; and generalize aviation-style integrity concepts (RAIM/ARAIM) to multi-modal autonomy. The distinctive contribution is a single sensor-to-assurance throughline in which every uncertainty representation is tied to its measurement physics, every fusion architecture is evaluated against an explicit integrity-monitoring requirement generalized from RAIM/ARAIM, and every safety-standard clause is mapped to a concrete architectural mechanism. We map these mechanisms onto ISO 26262, ISO 21448 (SOTIF), ISO/PAS 8800, ANSI/UL 4600, and the UNECE framework, and connect perception uncertainty to decision-making through chance-constrained MPC and formal safety filters (RSS, CBF). Industry case studies and emerging V2X and generative-simulation approaches close the loop to deployable safety arguments. Full article

Combine harvesters are evolving from machines equipped with isolated monitoring devices into distributed sensing platforms for process supervision, machine diagnosis, and adaptive control. This review summarizes representative research on six major sensing tasks in combine harvesters: grain loss, grain breakage, cleaning load, feed rate, grain-bin state, and grain quality. The reviewed studies are compared within a unified engineering framework that considers sensing target, installation position, signal path, disturbance source, calibration transferability, field robustness, and control relevance. Rather than evaluating sensors only as individual devices, this review emphasizes the coupled design of transducers, structural anti-interference measures, sampling paths, signal processing, and field-oriented validation under vibration-dominated and dust-laden harvesting conditions. The analysis shows that loss-rate and feed-rate sensing are currently the most mature and control-relevant categories, whereas breakage-rate, grain-bin, and integrated quality sensing remain constrained by representative sampling, disturbance resistance, and cross-condition generalization. Future progress will depend on multi-sensor fusion, realistic benchmark protocols, crop-aware calibration transfer, and tighter integration among onboard sensing, machine control, and digital harvesting systems. By clarifying the engineering value of these sensing routes, the review also supports loss reduction, quality preservation, labor-saving operation, and more reliable adaptive control in commercial grain harvesting. Full article

Multimodal image fusion is a promising technology designed to fuse information from different medical sensors, which offer structured insights for disease diagnosis and treatment. However, existing 2D-centric fusion methods fail to capture 3D spatial continuity, and conventional wavelet-based approaches lack adaptability to diverse lesion regions and suffer from background artifacts. To address this issue, we propose a 3D multiscale wavelet convolutional neural network for multimodal medical image fusion. Specifically, a 3D Discrete Wavelet Transformation (3D DWT) is introduced to decompose input volumes into multi-frequency bands, isolating anatomical structures and lesion details while reducing 3D spatial redundancy. We embed hierarchical multiple frequency band into a Global and Local Feature Calibration (GLFC) module to adaptively enhance single-modal features by fusing global contextual information and local details. Furthermore, a pyramid group-wise multiscale feature interaction is proposed for capturing complementary features across different spatial scales. Finally, a voxel-wise weighted averaging strategy reconstructs the fused image by adaptively assigning contributions to each modality at every spatial position, effectively eliminating artifacts and improving the visual fidelity of the result. Extensive experiments on the BraTS2020 and Hecktor datasets demonstrate that our proposed method outperforms state-of-the-art (SOTA) fusion methods in both subjective visual quality and objective metrics. Moreover, downstream segmentation validation confirms that fused images from our method significantly improve tumor segmentation accuracy. The source code and pre-trained models will be publicly available. Full article

Multi-sensor fusion of GNSS, IMU, and vision sensors has been extensively applied in urban Internet of Things systems and automated driving to improve positioning accuracy in complex environments. However, conventional FGO algorithms are based on fixed sensor weights, which limit their adaptability to fluctuations in sensor errors caused by environmental changes, thereby compromising positioning performance. To overcome this limitation, a novel multi-sensor adaptive weighted localization algorithm based on joint residuals detection was proposed in this study. The algorithm computes joint residuals by the sliding window accumulation of GNSS, IMU, and vision sensor measurements. By integrating a global weight decay factor into the M-estimation framework, the weights of each sensor were dynamically adjusted, thereby suppressing the effects of outliers on the state estimation. This approach enables high-precision and robust estimation of position, velocity, and attitude. Experimental results demonstrate that, based on validation with the GNSS–Visual–Inertial Navigation System (GVINS) public datasets sports field and complex environments, the proposed method exhibits superior performance in challenging low-altitude economic scenarios such as weak GNSS signals and significant IMU drift—specifically, it improves positioning accuracy by 32.3% and reduces velocity error by 32% compared to traditional FGO algorithms. In scenarios with GNSS signal interference, the system effectively mitigates error accumulation and maintains the stability of position and velocity estimation. The proposed algorithm demonstrates exceptional positioning accuracy and robustness in complex and dynamic environments, making it highly suitable for advanced urban IoT and automated driving applications. Full article

The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies. Full article

Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring. Full article

Scene understanding is a fundamental task in autonomous driving, requiring effective integration of semantic and geometric information from heterogeneous sensors. Although vision–language models (VLMs) provide powerful semantic representations, their integration with LiDAR-based geometric perception remains challenging. This paper proposes a multimodal late-fusion framework for multi-label scene classification that combines semantic embeddings extracted from camera images using a frozen CLIP (ViT-B/32) encoder with geometric features derived from LiDAR Bird’s-Eye-View (BEV) representations. To improve multimodal compatibility, modality-specific adaptation networks are employed to refine visual and geometric features before fusion. The proposed framework was evaluated on an annotated subset of the nuScenes dataset containing synchronized camera–LiDAR samples and nine scene-level labels. Experimental results show that the proposed late-fusion architecture outperforms both unimodal and early-fusion baselines, achieving a Hamming Accuracy of 0.950, a Micro-F1 score of 0.925, and a mean Average Precision (mAP) of 0.908. Additional experiments using a CLIP-based early-fusion baseline demonstrate that the observed performance gains are primarily attributable to the proposed modality-specific refinement and late-fusion strategy rather than the visual encoder alone. These findings indicate that modality-aware late fusion of pretrained semantic representations and LiDAR geometric information provides an effective and scalable solution for multimodal perception in autonomous driving. Full article

Camera and LiDAR provide highly complementary information, and effective fusion of both modalities is desirable for 3D object detection. However, existing decision-level fusion methods mainly rely on the confidence of objects while neglecting the object uncertainty. To address this, we propose UDF-3D, an uncertainty-driven camera–LiDAR decision-level fusion method based on Dempster–Shafer evidence theory. First, object uncertainty is quantified by introducing the theory of subjective logic, where subjective opinions incorporate category belief masses and an uncertainty mass. Second, a cost matrix is designed for object matching, where each element is a weighted combination of geometric and semantic information from both sensors, and the weights are determined by the uncertainty parameters. Third, we construct a view-frustum constraint to re-evaluate unmatched objects, thereby reducing the false-negative rate. Finally, we design a novel evidence discounting factor within the Dempster–Shafer framework for matched objects, thereby mitigating cross-modal object conflicts during fusion and improving detection accuracy. Experiments on the KITTI dataset demonstrate that the proposed method outperforms existing decision-level fusion approaches, yielding improved detection accuracy. Full article

To address the problems of false negatives, false positives, and delayed response in traditional fire detection systems, this paper proposes a warehouse fire detection scheme based on multi-sensor information fusion. By constructing a ZigBee wireless sensor network and integrating temperature, CO concentration and smoke sensors, fire simulation data are collected in the warehouse. At the data processing level, an improved Grubbs criterion is innovatively adopted to eliminate outliers, and the median is used instead of the average to effectively suppress the same-side shielding effect. At the feature layer fusion stage, a BP neural network model optimized by the cosine decreasing inertia weight particle swarm optimization algorithm (CIW-PSO) is designed. By dynamically adjusting the learning factors (c₁, c₂) and inertia weight (w), the convergence speed and global optimization ability are significantly improved. At the decision-making level, a fuzzy logic reasoning mechanism is introduced to integrate multi-parameter membership functions, thereby reducing the probability of misjudgment. Field tests have verified that the system can achieve early fire warning in a 50 m × 100 m warehouse environment, with a false alarm rate reduced by 42% compared to a single sensor and a response time shortened by 35%, providing an efficient and reliable intelligent solution for warehouse fire safety. Full article

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article

In industrial inspection, subtle defects often appear as local variations in appearance or geometry, making reliable anomaly detection challenging. A single sensing modality can miss important defect cues, while multimodal inspection combines appearance and geometric information to represent industrial objects more comprehensively. Many existing multimodal anomaly detection methods adopt early fusion strategies that integrate features at an early stage of the network. Such early integration can dilute modality-specific anomaly responses and cause anomaly smoothing, leading to degraded detection and localization performance. To address these challenges, we propose a reconstruction-based unsupervised multimodal anomaly detection framework integrating Discrepancy-Guided Complementary Fusion (DGCF) and Noise to Feature (N2F). Specifically, DGCF reduces anomaly smoothing by exploiting cross-modal discrepancies to extract complementary information, rather than directly summing or concatenating features from different modalities. Furthermore, N2F injects Gaussian noise into the feature space to regularize feature reconstruction and encourage the decoder to learn robust normal representations. Experimental results on the MVTec 3D-AD and Eyecandies datasets demonstrate the effectiveness of the proposed method. The proposed method achieves 97.3% I-AUROC, 99.6% P-AUROC, and 97.6% AUPRO on MVTec 3D-AD, and 94.8% I-AUROC, 98.6% P-AUROC, and 93.4% AUPRO on Eyecandies. Full article

Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work. Full article

Satellite-based vegetation monitoring has evolved from mapping vegetation canopy properties at single points in time toward analysing time-resolved dynamics of vegetation traits and process-related variables. Retrieved traits and solar-induced chlorophyll fluorescence (SIF) are inherently defined by sensor-specific spatial resolution, temporal integration, and spectral response. Modifying these characteristics alters the retrieval problem itself: under nonlinear retrievals and heterogeneous landscapes, aggregation and retrieval are generally non-commutative, and error components scale differently with resolution. Consequently, increasing spatial, spectral, or temporal detail does not guarantee improved ecological accuracy; a phenomenon we term the resolution–accuracy paradox. These interacting processes define the effective scale of vegetation products, which may differ from nominal sensor resolution and governs the interpretation of retrieved vegetation traits. When products with differing resolutions or compositing strategies are combined, scale effects can induce systematic artefacts in spatial patterns and derived dynamic indicators that cannot be resolved through improved per-pixel accuracy alone. This review establishes a scale-aware conceptual framework that treats scale as an explicit diagnostic dimension linking observation characteristics, retrieval formulations, trait definitions, and aggregation operators. We analyse how scale interactions influence spatial patterns, temporal dynamics, disturbance signals, and multiresolution data fusion, and derive diagnostic principles, best-practice guidelines, and research priorities for the scale-consistent interpretation of vegetation trait dynamics and SIF-constrained productivity and stress indicators across spatial and temporal scales. Framed in the context of upcoming hyperspectral missions such as CHIME and FLEX, which increase spectral information content, robust interpretation of vegetation products requires scale-consistent analysis and uncertainty-aware processing. For practitioners, this implies that vegetation products should be interpreted, validated, and compared at their effective scale rather than assuming that a finer spatial, spectral, or temporal resolution necessarily yields more reliable ecological information. Full article

Multispectral temperature measurement technology based on blackbody radiation theory has been widely applied in the field of non-contact temperature measurement. However, its applicability is limited by the single-point measurement mode. To address this limitation, this study developed a spectral fusion temperature measurement device and proposed a new method for reconstructing the two-dimensional temperature field of deflagration fireballs by fusing spectral and imaging data. The device adopts a CCD sensor and a fiber optic spectrometer placed in parallel with parallel optical axes. To ensure the accuracy of the CCD’s response characteristics at different distances, the photo-response non-uniformity (PRNU) calculation method was used for precision validation. In this study, spectral and imaging data of deflagration fireballs were obtained through experiments. Spectral data of consecutive frames at 189 ms, 192 ms, 195 ms, and 198 ms were extracted and analyzed, confirming that the temperature range at the four time points is 1050 K to 1800 K. The proposed method generates temperature elements with equal temperature intervals and their probabilities within the temperature range, and calculates the theoretical radiation spectrum of each element. Then, least squares optimization fitting is performed on the experimentally measured spectra to obtain the optimal probabilities of the temperature elements in the temperature field. By combining these optimal probabilities with CCD grayscale images, the 2D temperature distribution of the deflagration fireball was reconstructed. Results show that: the PRNU value of the device at a distance of 9 m is less than 2.2% through experimental verification; fused images of the temperature field spectra of four consecutive frames of the deflagration fireball were obtained using the proposed method. The average temperatures reconstructed by the proposed method at 189 ms, 192 ms, 195 ms, and 198 ms were 1382 K, 1373 K, 1366 K, and 1357 K, respectively, while the corresponding temperatures obtained by conventional spectral inversion were 1430 K, 1422 K, 1414 K, and 1406 K. The relative errors were 3.2%, 3.4%, 3.3%, and 3.4%, respectively, with an average relative error of approximately 3.3%. Full article

Stable three-dimensional hand landmark reconstruction using low-cost RGB-D sensors is important for human–computer interaction, robot teleoperation, and vision-based motion analysis. RGB-based hand landmark detectors provide stable semantic 2D landmarks, but their depth output is not a metric measurement in the physical camera coordinate system. Stereo cameras can provide metric depth, but direct landmark-level back-projection is sensitive to invalid pixels, local depth holes, boundary noise, and partial occlusion. To address these problems, this paper presents a lightweight RGB-D sensing front-end that combines MediaPipe semantic hand landmarks with ZED2 stereo depth. The proposed pipeline detects 21 semantic hand landmarks in the RGB image, obtains landmark-level metric depth from the aligned ZED2 depth map using local median sampling, reconstructs 3D landmarks by camera back-projection, and further applies exponential moving average filtering and a bone-length consistency constraint. Experiments were conducted on a self-collected SVO dataset containing 13 hand actions and 26 recorded sequences, and an additional checkerboard-based reference-distance validation was performed to evaluate the metric depth sampling and 3D back-projection component. Compared with single-pixel sampling, the $5\times 5$ local median strategy slightly increased the valid-depth ratio from 0.9731 to 0.9738 and reduced the temporal smoothness metric from 1.7163 mm to 1.6902 mm. To further justify the temporal filtering choice, an additional comparison with the 1 Euro Filter was conducted using the reconstructed win5 trajectories. The 1 Euro Filter produced stronger smoothing, reducing the temporal smoothness metric to 0.196 mm, but also reduced the path-length ratio to 0.484, indicating substantial motion attenuation. EMA0.7 was therefore retained as a more balanced setting, reducing the temporal smoothness metric to 0.826 mm while maintaining a path-length ratio of 0.803. The BL0.5 bone-length constraint reduced the bone-length standard deviation from 2.0727 mm to 1.1995 mm with limited trajectory modification. The final configuration provides a practical low-cost RGB-D front-end for stable 3D hand landmark reconstruction under controlled indoor conditions. Full article