Kalman filter
PulseAugur coverage of Kalman filter — every cluster mentioning Kalman filter across labs, papers, and developer communities, ranked by signal.
- instance of extended Kalman filter 90%
- instance of Unscented Kalman Filter 90%
- used by Gotit.pub 70%
- used by ScienceCast 70%
- used by CatalyzeX 70%
- used by DagsHub 70%
- other Unscented Kalman Filter 70%
- used by lidar 70%
- used by alphaXiv 70%
- used by extended Kalman filter 60%
- developed by extended Kalman filter 60%
- competes with extended Kalman filter 50%
3 day(s) with sentiment data
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Robotics localization basics covered with interactive tools
This item discusses the fundamentals of probabilistic localization in robotics, specifically focusing on the discrete Bayesian filter and the Kalman Filter. The author has created an interactive workbook using Google Co…
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New method optimizes privacy spending in machine learning training
Researchers have developed a new method for optimizing privacy allocation in machine learning, specifically for Differentially Private Stochastic Gradient Descent (DPSGD). The approach formulates the Gaussian noise-vari…
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AI-powered digital twin predicts cattle heat stress with 92% accuracy
Researchers have developed a novel digital twin framework for precision livestock farming, focusing on predicting core body temperature in dairy cattle to detect heat stress. This framework integrates physics-based simu…
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Transformer-aided Kalman filter enables monocular spacecraft pose estimation
Researchers have developed a novel pipeline for estimating the pose of unknown spacecraft using only monocular images. This approach integrates a transformer-based neural network with a Multi-State Constraint Kalman Fil…
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New LARK system offers low-cost, accurate tracking for image-guided surgery
Researchers have developed LARK, a new multi-camera optical tracking system designed for image-guided surgery. This system utilizes affordable RGB hardware and multi-view redundancy to overcome the occlusion issues and …
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New method finds cross-domain computational solutions by stripping field names
Researchers have developed a novel method to identify and import computational solutions across different scientific fields by stripping away domain-specific terminology and focusing on the underlying computational stru…
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New framework enhances target tracking through occlusion
Researchers have developed a new framework designed to improve target tracking in scenarios involving full and long-term occlusion. This system integrates YOLOv11n object detection with a Kalman Filter for motion predic…
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New LEQG Covariance Steering Problem Analyzed in Continuous Time
Researchers have formulated and analyzed the linear exponential quadratic Gaussian (LEQG) covariance steering problem in continuous time. This problem can be viewed as a risk-sensitive Schrödinger bridge between Gaussia…
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FILT3R layer enhances streaming 3D reconstruction with adaptive Kalman filter
Researchers have introduced FILT3R, a novel training-free layer designed to improve streaming 3D reconstruction by adaptively updating latent states. This method treats state updates as stochastic estimations in token s…
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New method enables cross-domain solution import for computational problems
Researchers have developed a novel method for identifying and importing computational solutions across different scientific fields. By creating domain-stripped computational fingerprints for research papers, they can ef…
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Kalman Delta Networks enhance language models with uncertainty-aware memory
Researchers have introduced Kalman Delta Networks (KDNs), a new family of models designed to enhance associative memory in language models by incorporating uncertainty awareness. These networks reformulate recurrent ass…
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Event-based camera data improves UAV forecasting with Kalman filter residuals
Researchers have developed a new method for forecasting Unmanned Aerial Vehicle (UAV) bounding boxes using event-based cameras. Their approach combines a constant-velocity Kalman filter with a residual model that predic…
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New Kalman filter algorithm enhances tilt estimation for robotics
Researchers have developed a new algorithm for tilt angle estimation, crucial for applications like robotics and motion tracking. This system utilizes an MPU6050 inertial measurement unit and an RP2040 microcontroller. …
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CERF framework slashes collaborative perception communication costs by 95%
Researchers have introduced CERF, a new framework designed to improve collaborative perception among multiple agents. This system addresses communication overhead and heterogeneity challenges by using a novel virtual mo…
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Neural networks optimize resin transfer moulding flow control
Researchers have developed a novel control strategy for resin transfer moulding that utilizes neural network surrogate models to optimize auxiliary gate pressures. This method aims to ensure complete saturation of fiber…
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New method adapts ROMs for unsteady flows using VAE and transformers
Researchers have developed a novel method for efficiently adapting Reduced Order Models (ROMs) in real-time for unsteady flow simulations. This approach utilizes a Variational Autoencoder (VAE) for dimensionality reduct…
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New framework fuses roadside radar and connected vehicle data for improved localization
Researchers have developed a new framework for vehicle localization that fuses data from roadside radar and connected vehicles. The study, conducted at an urban intersection in Helsinki, Finland, evaluated two extended …
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New MIMO Beam Mapping Framework for 6G Open RAN
Researchers have developed a novel framework for self-localizing MIMO beam mapping designed for intelligent Open RAN systems in future 6G networks. This system constructs a hierarchical wireless memory using sparse chan…
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New Bayesian framework improves structural health monitoring by removing EOV
Researchers have developed a novel Bayesian framework for structural health monitoring (SHM) that simultaneously identifies and removes environmental and operational variability (EOV). This approach models the latent EO…
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New Kalman Filter Method Boosts LLM RL Finetuning Efficiency
Researchers have developed a novel Kalman-Guided Prompt Selection (KGPS) method to improve the efficiency and effectiveness of reinforcement learning (RL) finetuning for large language models (LLMs). KGPS models prompt …