论文标题
持续计划惯性辅助系统
Continuous Planning for Inertial-Aided Systems
论文作者
论文摘要
惯性辅助系统需要连续的运动激发以及其他原因，以表征测量偏差，这将使本地化框架需要准确的集成。本文建议使用信息性的路径计划来找到最佳的轨迹，以最大程度地减少IMU偏见的不确定性和一种自适应痕迹方法，以指导计划者朝着有助于收敛的轨迹。关键的贡献是一种基于高斯过程（GP）的新型回归方法，以从RRT*计划算法的变体之间实现连续性和可不同的能力。我们采用应用于GP内核函数的线性操作员不仅推断连续位置轨迹，还可以推断速度和加速度。线性函数的使用实现了IMU测量给出的速度和加速度约束，将施加在位置GP模型上。模拟和现实世界实验的结果表明，IMU偏差收敛的计划有助于最大程度地减少状态估计框架中的本地化错误。
Inertial-aided systems require continuous motion excitation among other reasons to characterize the measurement biases that will enable accurate integration required for localization frameworks. This paper proposes the use of informative path planning to find the best trajectory for minimizing the uncertainty of IMU biases and an adaptive traces method to guide the planner towards trajectories which aid convergence. The key contribution is a novel regression method based on Gaussian Process (GP) to enforce continuity and differentiability between waypoints from a variant of the RRT* planning algorithm. We employ linear operators applied to the GP kernel function to infer not only continuous position trajectories, but also velocities and accelerations. The use of linear functionals enable velocity and acceleration constraints given by the IMU measurements to be imposed on the position GP model. The results from both simulation and real world experiments show that planning for IMU bias convergence helps minimize localization errors in state estimation frameworks.
