Abstract
Reliable localization without GPS is a prerequisite for autonomous unmanned aerial vehicles (UAVs) operating inside warehouses, where a lightweight monocular camera paired with an inertial measurement unit (IMU) and onboard computer are the minimal sensing and processing an onboard platform can carry. This paper presents a tightly coupled monocular point-feature visual-inertial odometry (VIO) system for that setting, realized on a GTSAM fixed-lag factor graph with inverse-depth landmarks, on-manifold IMU preintegration, and an online loop-closure pose graph. The system is developed as the initial estimation stage of an autonomous stock-management UAV under development for indoor logistics warehouses. The decisive design element is the bootstrap: the metric, gravity-aligned initialization of a monocular estimator is well conditioned only under a translation-rich trajectory, a condition the near-zero-baseline pickup and takeoff transient that opens every indoor flight violates. Building on the visual-inertial alignment of VINS-Mono, we harden this step with a pre-bundle-adjust conditioning gate and a continuous-window initialization that refines the whole bootstrap window inside the smoother instead of freezing a single seed. On all eleven EuRoC MAV sequences, indoor flight tests recorded onboard a micro air vehicle in an industrial hall and two instrumented rooms, one fixed configuration per operating environment converges on every sequence, including three that otherwise diverge by tens to thousands of meters, and, driven by the same feature stream as locally run VINS-Mono and PL-VINS baselines, attains the better pure-odometry accuracy on nine of the eleven, with ATE RMSE of 0.12–0.37 m on the Machine Hall, a margin a paired signed-rank test confirms against VINS-Mono and leaves unconfirmed against PL-VINS at this sample size. We identify the stock fixed-lag marginalization as the principal consistency limitation and outline First-Estimates-Jacobian marginalization as the route to a more consistent estimator, establishing a characterized point-only baseline on one public benchmark as the starting point for subsequent on-platform work.