4 accepted papers
In this paper, we introduce a novel estimator for vision-aided inertial navigation systems (VINS), the Preconditioned Cholesky-based Square Root Information Filter (PC-SRIF). When solving linear systems, employing Cholesky decomposition offers superior efficiency but can compromise numerical stabili
"Visual-inertial odometry (VIO) is the pose estimation backbone for most AR/VR and autonomous robotic systems today, in both academia and industry. However, these systems are highly sensitive to the initialization of key parameters such as sensor biases, gravity direction, and metric scale. In pract…
In this paper, we present a vision-aided inertial navigation system (VINS) for localizing wheeled robots. In particular, we prove that VINS has additional unobservable directions, such as the scale, when deployed on a ground vehicle that is constrained to move along straight lines or circular arcs.…
In this paper, we address the problem of cooperative mapping (CM) using datasets collected by multiple users at different times, when the transformation between the users' starting poses is unknown. Specifically, we formulate CM as a constrained optimization problem, where each user's independently