Abstract. Reliable visual localization is essential for long-range VTOL UAV mapping in GNSS-degraded environments. This paper presents a quantitative evaluation framework for monocular ORB-SLAM3 using a 66.48 km multi-altitude UAV mission and aerial-triangulation-derived camera poses as reference data. The workflow associates SLAM and reference trajectories by image key, applies Sim(3)-based metric alignment, corrects coordinate-axis inconsistency, and refines attitude by a global rotation offset, enabling full-mission and segment-level comparison in a common metric frame. The evaluation covers four altitude segments, namely 100, 150, 200, and 250 m AGL, under three protocols: No-Loop (NL), With-Loop Global Slice (GS), and With-Loop Local Re-Sim(3) (LR). For the full mission, the proposed alignment achieves a 3D position RMSE of 7.41 m over 5330 matched frames and substantially reduces the geometric deformation observed in the S+T baseline. Segment-level results show a strong altitude dependency in the isolated NL runs, with 3D RMSE decreasing from 22.95 m at 100 m to 5.49 m at 250 m. Among the three protocols, LR consistently yields the best segment-level position accuracy, reaching 4.00, 8.26, 3.94, and 3.92 m at 100, 150, 200, and 250 m, respectively. Long-range analysis further shows that the trajectory remains globally bounded, while cumulative 3D endpoint drift increases from 0.35 m at 50 m to 10.66 m at 25.6 km. These results indicate that ORB-SLAM3 can support large-scale trajectory estimation for UAV mapping, but its evaluated quality depends strongly on alignment, segmentation, and evaluation strategy.
Ming-Jyun Yang, J. Jhan, Runmeng Tang· The International Archives o...· 1 citation
Abstract. Navigating Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments requires reliable autonomous localization techniques. This study proposes a vision-based localization framework utilizing satellite true orthophotos and Digital Surface Models (DSMs) as absolute geospatial references. The algorithmic pipeline integrates deep learning architectures—specifically SuperPoint and LightGlue—to establish robust image-to-map feature correspondences. The matched correspondences are used to estimate camera exterior orientation parameters through collinearity-based spatial resection with an Iteratively Reweighted Least Squares (IRLS) approach. To validate the proposed methodology, a multi-altitude dataset (100–250 m) was acquired across structurally diverse terrains, including dense building, high vegetation, and bare ground areas. Experimental evaluations demonstrate that the framework achieves meter-level absolute positioning accuracy and stable pose estimation. Analyses further reveal that matching robustness and localization success rates depend heavily on terrain texture and flight altitude; geometrically structured urban scenes at moderate-to-high altitudes consistently yield reliable correspondences, whereas low-texture environments and lower flight altitudes present persistent challenges for continuous visual tracking.
Tai-Cyuan Wang, Lai-Han Tsou, J. Jhan et al.· The International Archives o...· 0 citations
Abstract. Traditional pavement inspection and data collection are often constrained by traffic conditions, operational safety, and equipment costs, making it difficult to achieve both efficiency and large-scale coverage. To address these limitations, this study employs a Pavement Roughness Index and Distress Extraction System (PRIDEs), which integrates high-resolution industrial cameras, high-precision global navigation satellite system (GNSS), wheel pulse sensors, and an onboard computer to acquire high-quality images under high-speed driving conditions. Using photogrammetry and computer vision techniques, camera poses are reconstructed to generate dense point clouds, digital surface models (DSMs), and orthophotos for detailed pavement distress analysis. However, the acquired imagery is affected by dynamic shadows and lens-focusing induced blur, resulting in ghosting artifacts and inconsistent orthophoto quality. To mitigate these issues, this study proposes a masking strategy during orthophoto generation, where U-Net is employed to detect shadow regions and Laplacian variance is used to identify blurred areas. By integrating these masks, more uniform and higher-quality orthophotos can be produced. Experimental results demonstrate that the proposed approach effectively reduces false positives and false negatives of crack detection caused by shadows and blur, thereby improving the reliability of orthophotos for automated pavement condition assessment.
Yueh-Che Li, J. Jhan· The International Archives o...· 0 citations
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