Continuous Planning for Inertial-Aided Systems

TL;DR

This paper introduces a novel Gaussian Process-based continuous planning method for inertial-aided systems, optimizing trajectories to improve IMU bias convergence and localization accuracy.

Contribution

It proposes a new GP regression approach integrated with RRT* for continuous, differentiable trajectories that incorporate IMU constraints, enhancing inertial navigation.

Findings

Planning trajectories for IMU bias convergence reduces localization errors.

The method outperforms traditional planning approaches in simulations and real-world tests.

Incorporating velocity and acceleration constraints improves system accuracy.

Abstract

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.