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The pose adjustment (PA) with a pose-only visual representation has beenproven equivalent to the bundle adjustment (BA), while significantly improvingthe computational efficiency. However, the pose-only solution has not yet beenproperly considered in a tightly-coupled visual-inertial state estimator (VISE)with a normal configuration for real-time navigation. In this study, we proposea tightly-coupled LiDAR-enhanced VISE, named PO-VINS, with a full pose-onlyform for visual and LiDAR-depth measurements. Based on the pose-only visualrepresentation, we derive the analytical depth uncertainty, which is thenemployed for rejecting LiDAR depth outliers. Besides, we propose a multi-stateconstraint (MSC)-based LiDAR-depth measurement model with a pose-only form, tobalance efficiency and robustness. The pose-only visual and LiDAR-depthmeasurements and the IMU-preintegration measurements are tightly integratedunder the factor graph optimization framework to perform efficient and accuratestate estimation. Exhaustive experimental results on private and publicdatasets indicate that the proposed PO-VINS yields improved or comparableaccuracy 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 thelaptop PC and the onboard ARM computer, respectively. Besides, PO-VINS yieldshigher accuracy and robustness than LE-VINS by employing the proposeduncertainty-based outlier-culling method and the MSC-based measurement modelfor LiDAR depth.

PO-VINS: An Efficient and Robust Pose-Only Visual-Inertial State Estimator With LiDAR Enhancement