Observer Design for Optical Flow-Based Visual-Inertial Odometry with Almost-Global Convergence

2025-08-28

AI Summary

Monocular visual-inertial odometry (VIO) suffers from local convergence bottlenecks in velocity and gravity direction estimation due to reliance on initial guesses and local linearization. Method: We propose a cascaded observer architecture with almost global asymptotic stability. First, a Riccati-type joint velocity–gravity observer is constructed in the body frame by fusing optical flow direction measurements with IMU data, ensuring global exponential stability. Second, a complementary observer on SO(3) estimates attitude. Third, spherical-constrained gradient descent optimizes optical flow direction usage. Results: Simulation results demonstrate that, under persistently exciting translational motion, the architecture simultaneously achieves high-accuracy, stable estimation of pose, body-frame velocity, and gravity direction. It overcomes classical VIO limitations—namely sensitivity to initialization and local convergence—and significantly improves robustness and applicability across diverse motion scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

Abstract

This paper presents a novel cascaded observer architecture that combines optical flow and IMU measurements to perform continuous monocular visual-inertial odometry (VIO). The proposed solution estimates body-frame velocity and gravity direction simultaneously by fusing velocity direction information from optical flow measurements with gyro and accelerometer data. This fusion is achieved using a globally exponentially stable Riccati observer, which operates under persistently exciting translational motion conditions. The estimated gravity direction in the body frame is then employed, along with an optional magnetometer measurement, to design a complementary observer on $mathbf{SO}(3)$ for attitude estimation. The resulting interconnected observer architecture is shown to be almost globally asymptotically stable. To extract the velocity direction from sparse optical flow data, a gradient descent algorithm is developed to solve a constrained minimization problem on the unit sphere. The effectiveness of the proposed algorithms is validated through simulation results.

Problem

• Estimating body velocity and gravity direction from optical flow and IMU measurements
• Developing stable observer architecture for visual-inertial odometry with global convergence
• Solving constrained minimization for velocity direction extraction from sparse optical flow

Innovation

• Cascaded observer fuses optical flow with IMU measurements
• Uses globally stable Riccati observer for velocity estimation
• Employs complementary SO(3) observer for attitude estimation