# Validation Log

## Validation Scenario 1: SP+LG for VO during Normal Flight

A UAV flies straight at 3fps. Each frame needs VO within 400ms.

### Expected Based on Conclusions
cuVSLAM: processes each frame in ~8.6ms, leaves 391ms for satellite matching and fusion. Immediate VO result via SSE.
SP+LG: processes each frame in ~130-280ms, leaves ~120-270ms. May interfere with satellite matching CUDA resources.

### Actual Validation
cuVSLAM is clearly superior. SP+LG offers no advantage here — cuVSLAM is 15-33x faster AND includes IMU fallback. SP+LG would require building a custom VO pipeline around a feature matcher, whereas cuVSLAM is a complete VO solution.

### Counterexamples
If cuVSLAM fails on nadir camera (its main risk), SP+LG could serve as a fallback VO method. But XFeat frame-to-frame (~30-50ms) is already identified as the cuVSLAM fallback and is 3-6x faster than SP+LG.

## Validation Scenario 2: LiteSAM at 1280px on Orin Nano Super

A keyframe needs satellite matching. Image is resized to 1280px for LiteSAM.

### Expected Based on Conclusions
LiteSAM at 1280px on Orin Nano Super: ~1.7-2.3s. This is 4-6x over the 400ms budget. Even running async, it means satellite corrections arrive 5-7 frames later.

### Actual Validation
1280px is LARGER than the paper's 1184px benchmark resolution. The user likely assumed we feed the full camera image (6252×4168) to LiteSAM, causing slowness. But the solution already downsamples. The bottleneck is the hardware performance gap (Orin Nano Super = ~25% of AGX Orin compute).

### Counterexamples
If LiteSAM's TensorRT FP16 engine with reparameterized MobileOne achieves better optimization than the paper's AMP benchmark (which uses PyTorch, not TensorRT), speed could improve 2-3x. At 480px with TensorRT FP16: potentially ~90-180ms on Orin Nano Super. This is worth benchmarking.

## Validation Scenario 3: SP+LG as Satellite Matcher After LiteSAM Abandonment

LiteSAM fails benchmark. Instead of XFeat, we try SP+LG for satellite matching.

### Expected Based on Conclusions
SP+LG: ~180-360ms on Orin Nano Super. Accuracy is worse than LiteSAM for cross-view matching.
XFeat: ~50-100ms. Accuracy is unproven on cross-view but general-purpose semi-dense.

### Actual Validation
SP+LG is 2-4x slower than XFeat and the LiteSAM paper confirms worse accuracy for satellite-aerial. XFeat's semi-dense approach is more suited to the texture-scarce regions common in UAV imagery. SP+LG's sparse keypoint detection may fail on agricultural fields or water bodies.

### Counterexamples
SP+LG could outperform XFeat on high-texture urban areas where sparse features are abundant. But the operational region (eastern Ukraine) is primarily agricultural, making this advantage unlikely.

## Review Checklist
- [x] Draft conclusions consistent with fact cards
- [x] No important dimensions missed
- [x] No over-extrapolation
- [x] Conclusions actionable/verifiable
- [ ] Note: Orin Nano Super estimates are extrapolated from AGX Orin data using the 3-4x compute ratio. Day-one benchmarking remains essential.

## Conclusions Requiring Revision
None — the original solution draft's architecture (cuVSLAM for VO, benchmark-driven LiteSAM/XFeat for satellite) is confirmed sound. SP+LG is not recommended for either role on this hardware.
