# Source Registry — SQ2 — Canonical pipeline decomposition

> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation).
> Critical-novelty sensitivity per Step 0.5 in `../00_question_decomposition.md`. Time windows applied:
> - **Lead-candidate / SOTA claims**: prefer sources within last 6 months; up to 18 months if older is the official authority.
> - **Library/SDK API behaviour**: must reflect the currently shipped version at search time (`context7` mandatory per lead candidate).
> - **Established baselines** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window.
>
> This file replaces a section of the previous monolithic `01_source_registry.md`. See `00_summary.md` for the full category index. Investigation order is tracked in `../00_question_decomposition.md` and the cross-category Investigation Status table in `00_summary.md`.

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### Source #38
- **Title**: Visual Place Recognition for Aerial Imagery: A Survey (Moskalenko, Kornilova, Ferrer — Skoltech)
- **Link**: https://arxiv.org/abs/2406.00885 (v2)
- **Tier**: L1 (peer-reviewed survey, accepted in Robotics and Autonomous Systems; companion benchmark code: https://github.com/prime-slam/aero-vloc)
- **Publication Date**: arXiv 2024-06; v2 update through 2024
- **Timeliness Status**: Currently valid (within 18-month authority window for established surveys; specific candidate latency numbers will need cross-validation against newer Jetson-class hardware reports)
- **Target Audience**: Aerial-VPR practitioners + UAV navigation system architects
- **Research Boundary Match**: **Full match** for the offline-cache visual geo-localization decomposition (aerial-nadir UAV vs. satellite tile basemap)
- **Summary**: Authoritative two-stage pipeline definition (verbatim): "Visual geolocalization can be implemented through various methods, typically relying on a pre-built database of images with known locations. This approach generally involves two stages: **global localization (or Visual Place Recognition, VPR) and local alignment**. Global localization involves identifying the nearest frame from the database (Image Retrieval), while local alignment determines the precise position using the selected frame." Re-ranking is treated as an integral sub-stage of VPR for aerial data because of agricultural/urban grid repetition. Local alignment = SuperPoint/keypoint detector → LightGlue/SuperGlue/SelaVPR matcher → cv2.findHomography → cv2.perspectiveTransform → Web-Mercator coordinate conversion. **Practitioner-critical runtime numbers (RTX 3090, NOT Jetson)**: AnyLoc descriptor calculation = 0.37–0.84 s/frame (huge ViT-G DINOv2); MixVPR / SALAD = 0.05–0.20 s; SelaVPR = 0.04 s; SuperGlue re-rank = 15–25 s on top-100 candidates; LightGlue re-rank = ~1 s; SelaVPR re-rank = <0.1 s. Memory: AnyLoc descriptors = 2.3–13.9 GB for 4–7k tiles; SelaVPR = <0.2 GB. Final commentary: "While our methodology alone may not provide comprehensive robustness, it can be effectively augmented with additional sensors, such as inertial measurement units (IMUs). This integration enhances its utility for Visual Inertial Odometry (VIO) and Simultaneous Localization and Mapping (SLAM) systems, particularly for periodic location refinement and loop closure tasks. Additionally, our methodology could serve as a dependable emergency localization fallback in the event of an unexpected GNSS signal loss." → **Validates the project's IMU/VIO + sat-anchor architecture as the canonical extension of the survey's two-stage core.**
- **Related Sub-question**: SQ2 (canonical decomposition), SQ3+SQ4 (C2/C3 candidate latency budgets), SQ5 (foundation-model-on-Jetson failure mode)


### Source #39
- **Title**: Cross-View Geo-Localization: A Survey (Durgam, Paheding, Dhiman, Devabhaktuni — U. Maine / Fairfield / ISU)
- **Link**: https://arxiv.org/abs/2406.09722 (v1)
- **Tier**: L1 (peer-style preprint, journal-bound — Expert Systems with Applications)
- **Publication Date**: arXiv 2024-06
- **Timeliness Status**: Currently valid (≤18 months for survey-of-deep-learning architectures)
- **Target Audience**: Cross-view (ground↔aerial) geo-localization researchers; partial overlap with our aerial↔satellite pipeline
- **Research Boundary Match**: **Partial — different cross-view setup** (the survey focuses on ground panorama → aerial overhead; ours is aerial nadir → satellite ortho). The pipeline-shape lessons transfer; the polar-transform / Siamese-network / GAN-based view-synthesis lessons do NOT directly apply because our two views are both top-down.
- **Summary**: Confirms the canonical pipeline decomposition (feature extraction → cross-view matching → similarity-driven retrieval) is the dominant pattern across 2015–2024 SOTA. Establishes the historical lineage: pixel-wise (Sheikh 2003) → feature-based (Lin 2013) → CNN/triplet-loss (Tian 2017) → Siamese+GAN (Hu 2018) → polar-transform (Shi 2019) → CosPlace/EigenPlaces (2022–2023) → DINOv2-class (AnyLoc 2023) → Transformer-only (TransGeo 2022, MGTL 2022) → multi-method fusion (2023+). Backbone comparison table establishes that ViT/DINOv2 is the current SOTA backbone; ResNet-class is the established production baseline; SIFT/SURF/PHOW remain the handcrafted baseline. **Confirms our component-area split (C2 VPR + C3 cross-domain matching) is canonical and matches the survey's two-axis organization (backbone × matching strategy).**
- **Related Sub-question**: SQ2 (decomposition lineage), SQ3+SQ4 (C2 candidate landscape)


### Source #40
- **Title**: OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata (Dhaouadi, Marin, Meier, Kaiser, Cremers — DeepScenario / TU Munich / MCML)
- **Link**: https://arxiv.org/abs/2509.18350 ; project page https://deepscenario.github.io/OrthoLoC
- **Tier**: L1 (peer-style preprint with public dataset, code, model checkpoints; 16,425 UAV images Germany+US, full 6-DoF ground truth)
- **Publication Date**: arXiv 2025-09 (within 6-month critical-novelty window)
- **Timeliness Status**: Currently valid (within 6-month critical-novelty window for SOTA aerial-localization claims)
- **Target Audience**: UAV-localization implementers + system architects building on Digital Orthophotos (DOP) + Digital Surface Models (DSM)
- **Research Boundary Match**: **Full match — direct paradigm match** to our project: "lightweight orthographic representations" instead of 3D meshes; "increasingly accessible through free releases by governmental authorities"; "no internet connection or GNSS/GPS support" — exactly the project's constraint envelope.
- **Summary**: **Most directly applicable SQ2 source.** Defines the 6-DoF localization pipeline using 2.5D geodata: (1) match query UAV image against DOP (orthophoto raster) using state-of-the-art matchers; (2) lift each 2D match in the DOP to 3D using the corresponding DSM elevation; (3) PnP+RANSAC (RANSAC-EPnP, 5-pixel inlier threshold) → initial pose; (4) Levenberg-Marquardt joint refinement of intrinsics + extrinsics; (5) **AdHoP refinement**: estimate homography from initial 2D-2D correspondences via DLT+RANSAC, warp the DOP to better match the query's perspective, re-match, map back via H⁻¹, lift to 3D, refine pose; accept refinement only if reprojection error decreases. **Quantitative results** on 16.4k images, 47 locations: best matcher = GIM+DKM achieves 75.4% recall at 1m-1° threshold (sparse SP+SG = 64.4%, sparse SP+LG = 64.2%, MASt3R = 63.5%, RoMa+AdHoP = 54.6%, XFeat*+AdHoP = 59.8%; LoFTR / eLoFTR / XoFTR all <23% recall). AdHoP yields ~30% average matching improvement, ~20% translation/rotation error reduction; for previously-underperforming methods (XFeat* → 95% matching improvement; DKM → 63% translation reduction; RoMa → 1m-1° recall +23%). **Performance factors** explicitly characterized: (a) **cross-domain DOPs (visual gap only) cause ~3× translation error increase** even on best method; (b) **cross-domain DOPs+DSMs (visual + structural gap) cause ~7× translation error increase** (0.16 m → 1.12 m for GIM+DKM+AdHoP) — **this is exactly the war-zone scene-change scenario AC-3.x covers**; (c) **20% covisibility floor** between query and reference; below it localization fails; (d) **Calibration is fundamentally ambiguous** between focal length and translation → camera intrinsics MUST be calibrated upstream, not jointly optimized in flight. (e) Resolution: scaling images to 30% of original (~300 px) still works; geodata at 13 m/pixel is the floor, with degradation below.
- **Related Sub-question**: SQ2 (canonical pipeline + AdHoP refinement loop), SQ3+SQ4 (C3 matcher candidate ranks), SQ5 (war-zone scene-change failure mode), SQ8 (covisibility safety gate)


### Source #41
- **Title**: Exploring the best way for UAV visual localization under Low-altitude Multi-view Observation Condition: a Benchmark — AnyVisLoc (Ye, Teng, Chen, Li, Liu, Yu, Tan — NUDT / Macao Polytechnic)
- **Link**: https://arxiv.org/abs/2503.10692 ; benchmark code https://github.com/UAV-AVL/Benchmark
- **Tier**: L1 (peer-style preprint with public 18,000-image dataset across 15 Chinese cities, multi-pitch / multi-altitude / multi-scene, with both aerial-photogrammetry AND satellite reference maps)
- **Publication Date**: arXiv 2025-03 (within 6-month critical-novelty window)
- **Timeliness Status**: Currently valid
- **Target Audience**: Aerial AVL practitioners; UAV-system designers facing pitch/altitude/yaw uncertainty
- **Research Boundary Match**: **Partial — different altitude regime** (the benchmark covers 30–300 m AGL, ours is ~1 km AGL); pitch range is 20–90° (ours is mostly nadir, ~80–90°). Lessons on the **pipeline structure, retrieval-vs-matching trade-offs, sensor-prior noise tolerance, and aerial-vs-satellite reference-map gap** transfer directly.
- **Summary**: Independently confirms the SAME pipeline as Source #40: image retrieval (rough position) → image matching (2D-2D) → DSM-lift to 3D → PnP+RANSAC. Best baseline = CAMP (retrieval) + RoMa (dense matcher) + Top-N re-rank → 74.1% A@5m on aerial photogrammetry map, 18.5% A@5m on satellite map (ALOS 30m DSM). **Critical AC-quantitative findings**: (a) **Aerial map vs satellite map**: 4× accuracy gap at A@5m (74.1% vs 18.5%) — driven by satellite-DSM coarseness (ALOS 30m vs aerial 0.94m) and modality difference. **Direct relevance**: project's offline cache is satellite tiles ≥0.5 m/px without DSM; this places us between the two data points (better than ALOS 30m, worse than aerial photogrammetry) — exact accuracy must be re-established once tile resolution is pinned. (b) **Yaw prior noise**: σ ≤ 5° → no impact; σ = 10° → 1.9% A@5m drop; σ = 30° → 4.1% drop; σ = 50° → 13.7% drop; σ = 60° → 25.7% drop. **Implication for project's C1+C5+IMU**: companion-side yaw estimate must hold σ < 10°. (c) **Pitch prior noise**: σ < 5° → no impact; σ ≥ 7° causes ~1–5% drops. (d) **Pitch angle**: smaller pitch (more oblique) → lower accuracy; nadir is best. Project's nadir-fixed camera at 1 km AGL is consistent with the benchmark's most-favourable regime. (e) **Sparse vs dense matchers**: SP+LightGlue+GIM+k2s = 75.4% A@10m at 105 ms/frame; RoMa = 81.3% A@10m at 659 ms/frame. **Implication for project's C7 Jetson runtime**: dense matchers ~6× more accurate but ~6× slower → SP+LightGlue-class is the production sweet spot under our 400 ms budget. (f) **Re-ranking strategy**: Top-N re-rank by inlier count = best accuracy/cost trade-off (62.2% A@5m at 0.8 s/frame on RTX 3090). Match-without-retrieval = catastrophic (34.3% A@5m, search-space too large).
- **Related Sub-question**: SQ2 (pipeline + sensor-prior tolerance), SQ3+SQ4 (C2 retrieval-vs-matcher trade-offs, C5 IMU prior contract), SQ5 (war-zone reference-map staleness failure mode), SQ7 (aerial-vs-satellite reference benchmarks)


### Source #42
- **Title**: Survey on absolute visual localization techniques for low-altitude unmanned aerial vehicles (Ye, Chen, Teng, Li, Yang, Song, Yu — NUDT, College of Aerospace Science)
- **Link**: https://www.sciopen.com/article/10.11887/j.issn.1001-2486.25120033 ; DOI 10.11887/j.issn.1001-2486.25120033
- **Tier**: L1 (peer-reviewed Chinese journal — Journal of National University of Defense Technology, vol 48 issue 2, 2026; same lab as Source #41 with overlapping authorship — confirmed cross-validation, not duplicative)
- **Publication Date**: 2026-04-01 (within 6-month critical-novelty window)
- **Timeliness Status**: Currently valid
- **Target Audience**: UAV-system architects + Chinese-defense-research community
- **Research Boundary Match**: **Full match** (low-altitude UAV AVL is the survey's exact subject)
- **Summary**: Survey-level confirmation of the canonical "**retrieval-matching-pose estimation**" hierarchical framework. Verbatim claim: "the hierarchical framework balances search efficiency, positioning accuracy, and scene generalization, becoming a robust technical path for low-altitude long-endurance absolute localization." Compares the framework against alternatives that are explicitly rejected: (a) relative visual localization (cumulative errors — VIO/SLAM only); (b) end-to-end direct localization (poor generalization); (c) map-free localization (scene-dependent). Sub-component evolution per stage: (a) retrieval = template-matching (SAD/SSD/NCC) → BoW/VLAD → deep-learning (annular/dense feature segmentation, contrastive InfoNCE, self-supervised); (b) matching = SIFT/SURF/ORB → SuperPoint+LightGlue/RoMa (sparse / semi-dense / dense); (c) pose estimation = PnP variants + RANSAC + IMU prior fusion. **Identifies four open challenges** that align with project risks: (i) cross-domain generalization (war-zone scene change); (ii) real-time inference on edge platforms (Jetson); (iii) robustness to complex environments (cropland, snow, low texture); (iv) high-quality datasets (the same gap our project's AC-NEW-7 / cache provisioning works around). **Lightweight-model-design-for-edge-deployment is named as a primary future-research direction** — directly validates project's Jetson Orin Nano constraint as a recognized field-level challenge, not a project-specific oddity.
- **Related Sub-question**: SQ2 (framework canonicalness), SQ3+SQ4 (per-component evolution), SQ5 (named open challenges align with project risks)

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## SQ3+SQ4 / C1 (Visual / Visual-Inertial Odometry) — Candidate enumeration
