3 accepted papers
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
Parallel tracking and mapping (PTAM) as a time-efficient framework for simultaneous localization and mapping (SLAM) has been becoming popular in recent years. However, in this paper, we vigilantly point out that the favorite parallel-pipeline design realized by recent proposed SLAM algorithms may le…
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…