Depth-Assisted Sparse Visual Odometry for UAV-Relevant Synthetic RGB-D Evaluation: A Controlled Geometric-Backend Ablation

Sparse visual odometry (VO) is a core component of lightweight unmanned aerial vehicle (UAV) visual navigation, yet the isolated effect of adding aligned metric depth to a minimal frame-to-frame pipeline is easily obscured in full SLAM systems. This paper presents a UAV-relevant controlled synthetic ablation of RGB-only and RGB-D geometric backends under fixed sparse frontends. ORB matching and KLT tracking are evaluated on a 32-sequence TartanAir validation split of flight-like synthetic RGB-D scenes by routing identical 2D correspondences either to Essential Matrix estimation or, with aligned depth, to PnP with RANSAC. The study reports ATE, Sim(3)-aligned ATE, translational and rotational RPE, robustness under temporal subsampling and RGB degradations, and isolated solver latency on a workstation and Raspberry Pi 4. At stride 1, RGB-D PnP reduces ATE by 61.8% for KLT and 29.1% for ORB, with translational RPE reductions of 61.6% and 41.9%. Rotational RPE reductions are stronger and persist across all tested strides, reaching 85.9% for KLT and 77.3% for ORB at stride 1. Sim(3) analysis shows that only 7–16% of PnP ATE is metric-scale drift. At coarser strides, however, KLT-PnP no longer improves ATE, showing that depth assistance depends on stable frontend tracking and valid depth-supported correspondences. The contribution is a reproducible diagnostic benchmark and failure-mode analysis for UAV-relevant depth-assisted sparse VO under oracle aligned depth, providing component-level evidence rather than full onboard deployment validation.