Dual Quaternion-Based Unscented Kalman Filter with Visual Inertial Odometry for Navigation in GPS-Denied Environments
2026-06-08 • Robotics
Robotics
AI summaryⓘ
The authors developed a new method to help robots and drones know where they are when GPS signals are not available. They combined a special math technique called Dual Quaternion-Based Unscented Kalman Filter (DQUKF) with a system that uses camera images and motion sensors (Visual Inertial Odometry). Their method tracks how the device moves and corrects its position using both visual and sensor information. Tests showed their approach is accurate and beats other similar methods in challenging situations. This helps devices navigate reliably without GPS.
Dual QuaternionUnscented Kalman FilterVisual Inertial OdometryState EstimationError StateIMUSigma PointsCovariance PropagationEuRoC MAV DatasetPosition RMSE
Authors
Mohamed Khalifa, Hashim A. Hashim
Abstract
Reliable navigation in GPS-denied environments remains a fundamental challenge in robotics, aerospace, and autonomous vehicle applications. This paper presents a Dual Quaternion-Based Unscented Kalman Filter (DQUKF) equipped with a Visual Inertial Odometry (VIO) algorithm for accurate state estimation enabling navigation in GPS denied locations. The proposed framework formulates the DQUKF in an error state manner, where the nominal pose is represented by a unit dual quaternion and the local pose error is represented by a 6-dimensional twistor parameterization used for sigma point generation, covariance propagation, and measurement correction. In parallel, the VIO algorithm tracks features across image frames, synchronizes measurements between the IMU and camera, and provides visual constraints that complement inertial propagation. Simulation results on the EuRoC MAV dataset show that the proposed DQUKF converges under high initialization uncertainty and achieves a position RMSE of 0.2584~m in the difficult flight sequence, outperforming the benchmark filters.