Kalman Filtering

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Overview

Kalman filtering studies how to track a system’s changing, hidden state from noisy observations, using uncertainty to decide how much to trust each prediction and measurement. Linear Kalman filters and nonlinear variants such as the extended, unscented, and error-state filters are widely used in robot navigation, visual-inertial odometry, multi-object tracking, and data assimilation; evaluations often use settings such as KITTI, EuRoC, and nuScenes. Current approaches adapt noise covariances, handle delayed or distributed measurements, or combine recursive filters with neural networks and generative methods to cope with nonlinear dynamics and imperfect sensors. A central challenge is improving accuracy under model mismatch without sacrificing calibrated uncertainty, consistency, interpretability, or real-time efficiency.

Latest papers

CardsList

73 papers5 papers in the last four weeks, down 37% on the four weeks before. 0.0% of all new papers.

  1. Neural Aided Adaptive Innovation-Based Invariant Kalman Filter

    Mar 17, 2026Barak Diker, Itzik KleinKalman FilteringInvariant EKF

  2. Robust Automatic Differentiation of Square-Root Kalman Filters via Gramian Differentials

    Mar 13, 2026Adrien CorenflosAutomatic DifferentiationKalman Filtering

    Department of Statistics, University of Warwick, Coventry, UK

  3. Consistent and Efficient MSCKF-based LiDAR-Inertial Odometry with Inferred Cluster-to-Plane Constraints for UAVs

    Mar 13, 2026Jinwen Zhu, Xudong Zhao, Fangcheng Zhu +4LiDAR OdometryRobot State Estimation

    Meituan UAV, Beijing, China · Dept. of Mechanical Engineering, Computer and Information Sciences, University of Delaware, Newark, DE

  4. From Gradients to Riccati Geometry: Kalman World Models for Single-Pass Learning

    Mar 13, 2026Andrew KirulutaGradient-Free OptimizationState Space Models

    UC Berkeley School of Information

  5. Distributed Kalman--Consensus Filtering with Adaptive Uncertainty Weighting for Multi-Object Tracking in Mobile Robot Networks

    Mar 11, 2026Niusha Khosravi, Rodrigo Ventura, Meysam BasiriMulti-Object TrackingMulti-Robot Systems

    Instituto Superior Técnico University of Lisbon Lisbon, Portugal

  6. Outlier-robust Autocovariance Least Square Estimation via Iteratively Reweighted Least Square

    Mar 9, 2026Jiahong Li, Fang DengCovariance EstimationRobust Estimation

    College of Robotics, Beijing Union University, Beijing 100101, China · Key Laboratory of Intelligent Control and Decision of Complex Systems, and also with the School of Automation, Beijing Institute of Technology, Beijing 100081, China

  7. Robust Tightly-Coupled Filter-Based Monocular Visual-Inertial State Estimation and Graph-Based Evaluation for Autonomous Drone Racing

    Mar 3, 2026Maulana Bisyir Azhari, Donghun Han, Sung Jun Park +1Autonomous Drone RacingError-State Kalman Filtering

    Unmanned Systems Research Group (USRG), School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea.

  8. Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering

    Mar 2, 2026Hari Prasanth S. M., Pejman Habibiroudkenar, Eerik Alamikkotervo +2Trajectory PredictionKalman Filtering

    Department of Energy and Mechanical Engineering, Aalto University, 02150 Espoo, Finland

  9. A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking

    Feb 17, 2026Jose Luis Peralta-Cabezas, Miguel Torres-Torriti, Marcelo Guarini-HermannRobot Trajectory TrackingRobot State Estimation

    Dept. of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile

  10. Kalman Filtering Based Flight Management System Modeling for AAM Aircraft

    Feb 16, 2026Balram Kandoria, Aryaman Singh SamyalKalman FilteringUncertainty Quantification

    SkyGrid, Austin, TX, 78727

  11. Kalman-Bucy Filtering with Randomized Sensing: Fundamental Limits and Sensor Network Design for Field Estimation

    Oct 29, 2025Xinyi Wang, Devansh R. Agrawal, Dimitra PanagouKalman Filtering

    Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA · Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA · Departments of Robotics and Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA

  12. Flow Matching for Efficient and Scalable Data Assimilation

    Aug 18, 2025Taos Transue, Bohan Chen, So Takao +1Flow MatchingKalman Filtering

    Department of Mathematics and Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, 84112 · The Computing and Mathematical Sciences Department, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125

  13. Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking

    May 12, 2025Mohamed Nagy, Naoufel Werghi, Bilal Hassan +2Kalman Filtering3D MOT

    Khalifa University, Abu Dhabi, UAE · New York University of Abu Dhabi