Robocentric Visual-Inertial Odometry

In this paper, we propose a novel robocentric formulation of visual-inertial navigation systems (VINS) within a multi-state constraint Kalman filter (MSCKF) framework and develop an efficient, lightweight, robocentric visual-inertial odometry (R-VIO) algorithm for consistent localization in challenging environments using only monocular vision. The key idea of the proposed approach is to deliberately reformulate the 3D VINS with respect to a moving local frame (i.e., robocentric), rather than a fixed global frame of reference as in the standard world-centric VINS, and instead utilize high-accuracy relative motion estimates for global pose update. As an immediate advantage of using this robocentric formulation, the proposed RVIO can start from an arbitrary pose, without the need to align its orientation with the global gravity vector. More importantly, we analytically show that the proposed robocentric EKF-based VINS does not undergo the observability mismatch issue as in the standard world-centric frameworks which was identified as the main cause of inconsistency of estimation. The proposed RVIO is extensively tested through both Monte Carlo simulations and real-world experiments using different sensor platforms in different environments and shown to achieve competitive performance with the state-of-the-art VINS algorithms in terms of consistency, accuracy and efficiency.