Robotics
♻ ★ PO-VINS: An Efficient and Robust Pose-Only Visual-Inertial State Estimator With LiDAR Enhancement
The pose adjustment (PA) with a pose-only visual representation has been
proven equivalent to the bundle adjustment (BA), while significantly improving
the computational efficiency. However, the pose-only solution has not yet been
properly considered in a tightly-coupled visual-inertial state estimator (VISE)
with a normal configuration for real-time navigation. In this study, we propose
a tightly-coupled LiDAR-enhanced VISE, named PO-VINS, with a full pose-only
form for visual and LiDAR-depth measurements. Based on the pose-only visual
representation, we derive the analytical depth uncertainty, which is then
employed for rejecting LiDAR depth outliers. Besides, we propose a multi-state
constraint (MSC)-based LiDAR-depth measurement model with a pose-only form, to
balance efficiency and robustness. The pose-only visual and LiDAR-depth
measurements and the IMU-preintegration measurements are tightly integrated
under the factor graph optimization framework to perform efficient and accurate
state estimation. Exhaustive experimental results on private and public
datasets indicate that the proposed PO-VINS yields improved or comparable
accuracy to sate-of-the-art methods. Compared to the baseline method LE-VINS,
the state-estimation efficiency of PO-VINS is improved by 33% and 56% on the
laptop PC and the onboard ARM computer, respectively. Besides, PO-VINS yields
higher accuracy and robustness than LE-VINS by employing the proposed
uncertainty-based outlier-culling method and the MSC-based measurement model
for LiDAR depth.
comment: 17 pages, 13 figures, 8 tables