# Comparison Framework

## Selected Framework Type
Decision Support + Problem Diagnosis

## Selected Dimensions
1. Inference speed on Orin Nano Super
2. Accuracy for the target task
3. Cross-view robustness (satellite-aerial gap)
4. Implementation complexity / ecosystem maturity
5. Memory footprint
6. TensorRT optimization readiness

## Comparison 1: Visual Odometry — cuVSLAM vs SuperPoint+LightGlue

| Dimension | cuVSLAM v15.0.0 | SuperPoint + LightGlue (TRT) | Factual Basis |
|-----------|-----------------|-------------------------------|---------------|
| Speed on Orin Nano | ~8.6ms/frame (116fps @ 720p) | Est. ~150-300ms/frame (SP ~40-60ms + LG ~100-200ms) | Fact #1, #2, #3 |
| VO accuracy (KITTI) | <1% trajectory error | ~1% odometric error (desktop) | Fact #1, #2 |
| VO accuracy (EuRoC) | <5cm position error | Not benchmarked | Fact #1 |
| IMU integration | Native mono+IMU mode, auto-fallback | None — must add custom IMU fusion | Fact #1 |
| Loop closure | Built-in | Not available | Fact #1 |
| TensorRT ready | Native CUDA (not TensorRT, raw CUDA) | Requires ONNX export + TRT build | Fact #4 |
| Memory | ~200-300MB | SP ~50MB + LG ~50-100MB = ~100-150MB | Fact #1 |
| Implementation | pip install aarch64 wheel | Custom pipeline: SP export + LG export + matching + pose estimation | Fact #1, #4 |
| Maturity on Jetson | NVIDIA-maintained, production-ready | Community TRT plugins, limited Jetson benchmarks | Fact #4, #5 |

## Comparison 2: LiteSAM Speed at Different Resolutions

| Dimension | 1184px (paper default) | 1280px (user proposal) | 640px | 480px | Factual Basis |
|-----------|------------------------|------------------------|-------|-------|---------------|
| Tokens at 1/8 scale | ~21,904 | ~25,600 | ~6,400 | ~3,600 | Fact #7 |
| AGX Orin time | 497ms | Est. ~580ms (1.17x tokens) | Est. ~150ms | Est. ~90ms | Fact #5, #7 |
| Orin Nano Super time (est.) | ~1.5-2.0s | ~1.7-2.3s | ~450-600ms | ~270-360ms | Fact #5, #6 |
| Accuracy (RMSE@30) | 17.86m | Similar (slightly less) | Degraded | Significantly degraded | Fact #8, #10 |

## Comparison 3: Satellite Matching — LiteSAM vs SP+LG vs XFeat

| Dimension | LiteSAM (opt) | SuperPoint+LightGlue | XFeat semi-dense | Factual Basis |
|-----------|--------------|---------------------|------------------|---------------|
| Cross-view accuracy | RMSE@30 = 17.86m (UAV-VisLoc) | Worse than LiteSAM (paper confirms) | Not benchmarked on UAV-VisLoc | Fact #9, #10 |
| Speed on Orin Nano (est.) | ~1.5-2s @ 1184px, ~270-360ms @ 480px | Est. ~100-200ms total | ~50-100ms | Fact #5, #2, existing draft |
| Cross-view robustness | Designed for satellite-aerial gap | Sparse matcher, "lacks sufficient accuracy" for cross-view | General-purpose, less robust | Fact #9, #13 |
| Parameters | 6.31M | SP ~1.3M + LG ~7M = ~8.3M | ~5M | Fact #5 |
| Approach | Semi-dense (coarse-to-fine, subpixel) | Sparse (detect → match → verify) | Semi-dense (detect → KNN → refine) | Fact #1, existing draft |
