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.
Abstract (translated)
这篇论文提出了一种新颖的级联观测器架构，该架构结合了光流和惯性测量单元（IMU）的数据来执行连续单目视觉惯性里程计（VIO）。所提出的解决方案通过融合来自光流测量的速度方向信息与陀螺仪和加速度计数据，在同一个框架内同时估计机体坐标系中的速度和重力方向。这种融合是利用一个在持续激励平移运动条件下全局指数稳定的里卡蒂观测器来实现的。 然后，以机体系中估算出的重力方向为基础，并可选择性地结合磁力计测量值，设计了一个定义在$\mathbf{SO}(3)$上的互补观测器来进行姿态估计。所得到的互联观测器架构被证明是几乎全局渐近稳定的。 为了从稀疏光流数据中提取速度方向，开发了一种梯度下降算法来解决单位球面上的一个约束最小化问题。通过仿真结果验证了所提出的算法的有效性。
URL
https://arxiv.org/abs/2508.21163
