Square-Root Robocentric Visual-Inertial Odometry With Online Spatiotemporal Calibration

Abstract

Robocentric visual-inertial odometry (R-VIO) in our recent work [1] models the probabilistic state estimation problem with respect to a moving local (body) frame, which is contrary to a fixed global (world) frame as in the world-centric formulation, thus avoiding the observability mismatch issue and achieving better estimation consistency. To further improve efficiency and robustness in order to be amenable for the resource-constrained applications, in this paper, we propose a novel information-based estimator, termed R-VIO2. In particular, the numerical stability and computational efficiency are significantly boosted by using i) the square-root expression and ii) incremental QR-based update combined with back substitution. Moreover, the spatial transformation and time offset between visual and inertial sensors are jointly calibrated online to robustify the estimator performance in the presence of unknown parameter errors. The proposed R-VIO2 has been extensively tested on public benchmark dataset as well as in a large-scale real-world experiment, and shown to achieve very competitive accuracy and superior time efficiency against the state-of-the-art visual-inertial navigation methods.