融合光流与惯性数据，实现高精度单目视觉惯性里程计
Observer Design for Optical Flow-Based Visual-Inertial Odometry with Almost-Global Convergence
- 分层观测器融合光流方向与IMU数据
- 在持续激励运动下实现全局指数稳定
- 适合机器人导航与自动驾驶系统
本文提出一种新型级联观测器架构，结合光流与惯性测量单元（IMU）数据，实现连续的单目视觉惯性里程计（VIO）。该方法通过融合光流提供的速度方向信息与陀螺仪和加速度计数据，同时估计机体帧下的速度与重力方向。该融合过程基于全局指数稳定的Riccati观测器，在持续激励的平移运动条件下有效运行。所估计的机体帧重力方向，结合可选的地磁测量，用于设计SO(3)上的互补观测器以实现姿态估计。整体互联观测器架构被证明具有几乎全局渐近稳定性。为从稀疏光流数据中提取速度方向，开发了一种在单位球面上求解约束最小化问题的梯度下降算法。仿真结果验证了所提算法的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。