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.
翻译:惰性辅助系统需要不断的动作刺激,以说明测量偏差的特点,从而能够准确整合本地化框架所需的地方化框架。本文件提议使用信息化路径规划,以找到尽可能减少IMU偏向不确定性的最佳轨迹,并采用适应性跟踪方法指导规划人员走向有助于趋同的轨迹。关键贡献是基于高山进程的一种新的回归方法,以强制保持与RRT*规划算法的变式不同途径点之间的连续性和差异性。我们使用应用于GP内核函数的线性操作员,不仅推断连续位置轨迹,而且还推断速度和加速。使用线性功能使IMU测量结果所设定的速度和加速限制能够施加在GP模型的位置上。模拟和实际世界实验的结果显示,IMU偏差趋同的规划有助于尽量减少国家估算框架中的本地化错误。