In this paper, we propose a novel robocentric formulation of the
visual-inertial navigation system (VINS) within a sliding-window filtering
framework and design an efficient, lightweight, robocentric visual-inertial
odometry (R-VIO) algorithm for consistent motion tracking even in challenging
environments using only a monocular camera and a 6-axis IMU. The key idea is to
deliberately reformulate the VINS with respect to a moving local frame, rather
than a fixed global frame of reference as in the standard world-centric VINS,
in order to obtain relative motion estimates of higher accuracy for updating
global poses. As an immediate advantage of this robocentric formulation, the
proposed R-VIO can start from an arbitrary pose, without the need to align the
initial orientation with the global gravitational direction. More importantly,
we analytically show that the linearized robocentric VINS does not undergo the
observability mismatch issue as in the standard world-centric counterpart which
was identified in the literature as the main cause of estimation inconsistency.
Additionally, we investigate in-depth the special motions that degrade the
performance in the world-centric formulation and show that such degenerate
cases can be easily compensated in the proposed robocentric formulation,
without resorting to additional sensors as in the world-centric formulation,
thus leading to better robustness. The proposed R-VIO algorithm has been
extensively tested through both Monte Carlo simulations and real-world
experiments with different sensor platforms navigating in different
environments, and shown to achieve better (or competitive at least) performance
than the state-of-the-art VINS, in terms of consistency, accuracy and
efficiency.

本文提出了一种新颖的机器人中心视觉惯性导航系统的公式，在滑动窗口滤波框架内进行设计，使用单眼相机和 6 轴 IMU 实现高精度的运动跟踪，将 VINS 相对于运动的局部坐标系进行重构，以获得更高精度的相对运动估计，并提出 R-VIO 算法。该算法在 Monte Carlo 的模拟实验和真实环境实验中进行了广泛测试，并显示出在一些方面打破了当前技术的局限性。