Adaptive Covariance and Quaternion-Focused Hybrid Error-State EKF/UKF for Visual-Inertial Odometry

• URL: http://arxiv.org/abs/2512.17505v1
• Date: Fri, 19 Dec 2025 12:14:37 GMT
• Title: Adaptive Covariance and Quaternion-Focused Hybrid Error-State EKF/UKF for Visual-Inertial Odometry
• Authors: Ufuk Asil, Efendi Nasibov,
• Abstract summary: This study presents an innovative hybrid Visual-Inertial Odometry (VIO) method for Unmanned Aerial Vehicles (UAVs)<n>The system utilizes a novel hybrid Quaternion-focused Error-State EKF/UKF architecture to process inertial measurement unit (IMU) data.<n>The reliability of visual measurements is assessed via a dynamic sensor confidence score based on metrics, such as image entropy variation, motion blur, and inference quality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents an innovative hybrid Visual-Inertial Odometry (VIO) method for Unmanned Aerial Vehicles (UAVs) that is resilient to environmental challenges and capable of dynamically assessing sensor reliability. Built upon a loosely coupled sensor fusion architecture, the system utilizes a novel hybrid Quaternion-focused Error-State EKF/UKF (Qf-ES-EKF/UKF) architecture to process inertial measurement unit (IMU) data. This architecture first propagates the entire state using an Error-State Extended Kalman Filter (ESKF) and then applies a targeted Scaled Unscented Kalman Filter (SUKF) step to refine only the orientation. This sequential process blends the accuracy of SUKF in quaternion estimation with the overall computational efficiency of ESKF. The reliability of visual measurements is assessed via a dynamic sensor confidence score based on metrics, such as image entropy, intensity variation, motion blur, and inference quality, adapting the measurement noise covariance to ensure stable pose estimation even under challenging conditions. Comprehensive experimental analyses on the EuRoC MAV dataset demonstrate key advantages: an average improvement of 49% in position accuracy in challenging scenarios, an average of 57% in rotation accuracy over ESKF-based methods, and SUKF-comparable accuracy achieved with approximately 48% lower computational cost than a full SUKF implementation. These findings demonstrate that the presented approach strikes an effective balance between computational efficiency and estimation accuracy, and significantly enhances UAV pose estimation performance in complex environments with varying sensor reliability.

Related papers

• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM [5.5217350574838875]
  We present ICD-Net, a novel framework that enhances visual-inertial SLAM performance.<n>Our approach significantly improved trajectory estimation accuracy compared to standard VINS-Fusion.<n>Our method shows that neural network enhancement can effectively address multiple sources of SLAM degradation.
  arXiv  Detail & Related papers  (2025-11-13T11:25:02Z)
• Hierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models [0.479839492673697]
  The Hierarchical Evaluation Function (HEF) is proposed as a multi-metric framework for hyperparameter optimization.<n>HEF integrates explanatory power (R2), sensitivity to extreme errors (RMSE), and average accuracy (MAE)<n>The performance of HEF was assessed using four widely recognized benchmark datasets in the forecasting domain.
  arXiv  Detail & Related papers  (2025-08-18T16:25:49Z)
• Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers [0.0]
  This work presents a complete pipeline integrating advanced computer vision techniques with adaptive nonlinear filtering.<n>A Convolutional Neural Network (CNN), enhanced with image preprocessing, detects structural markers from chaser imagery.<n>A UKF framework is selected for its ability to handle nonlinear relative dynamics to estimate the full relative pose.
  arXiv  Detail & Related papers  (2025-07-22T04:13:03Z)
• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [84.78366452133514]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems [89.35169042718739]
  collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data to cloud servers.<n>Recent studies have revealed that these intermediate features may not sufficiently preserve privacy, as information can be leaked and raw data can be reconstructed via model inversion attacks (MIAs)<n>This work first theoretically proves that the conditional entropy of inputs given intermediate features provides a guaranteed lower bound on the reconstruction mean square error (MSE) under any MIA.<n>Then, we derive a differentiable and solvable measure for bounding this conditional entropy based on the Gaussian mixture estimation and propose a conditional entropy algorithm to enhance the inversion robustness
  arXiv  Detail & Related papers  (2025-03-01T07:15:21Z)
• Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems [0.30723404270319693]
  This paper introduces an enhanced FL framework with three key innovations: adaptive model aggregation based on sensor reliability, dynamic node selection for resource optimization, and Weibull-based checkpointing for fault tolerance.<n> Experiments on the NASA Bearing and Hydraulic System datasets demonstrate superior performance compared to state-of-the-art FL methods, achieving 99.5% AUC-ROC in anomaly detection and maintaining accuracy even under node failures.
  arXiv  Detail & Related papers  (2025-01-28T03:04:47Z)
• Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction [46.76846936581471]
  In Sequential Recommendation Systems (SRecsys), traditional training approaches that rely on Cross-Entropy (CE) loss often prioritize accuracy but fail to align well with user satisfaction metrics.<n>We propose textbfCPFT, a novel fine-tuning framework that integrates Conformal Prediction (CP)-based losses with CE loss to optimize accuracy alongside confidence that better aligns with widely used top-$K$ metrics.
  arXiv  Detail & Related papers  (2024-02-14T06:43:02Z)
• Adaptive Kalman-Informed Transformer [13.221163846643607]
  The extended Kalman filter (EKF) is a widely adopted method for sensor fusion in navigation applications.<n>We derive an adaptive Kalman-informed transformer (A-KIT) designed to learn the varying process noise covariance online.<n>We show that A-KIT outperforms the conventional EKF by more than 49.5% and model-based adaptive EKF by an average of 35.4% in terms of position accuracy.
  arXiv  Detail & Related papers  (2024-01-18T14:04:51Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.