gps-denied-desktop/docs/03_tests/33_accuracy_20m_high_precision_spec.md

Acceptance Test: AC-2 - 60% of Photos < 20m Error

Summary

Validate Acceptance Criterion 2: "The system should find out the GPS of centers of 60% of the photos from the flight within an error of no more than 20 meters in comparison to the real GPS."

Linked Acceptance Criteria

AC-2: 60% of photos < 20m error

Preconditions

1. ASTRAL-Next system fully operational
2. All TensorRT models loaded (FP16 precision for maximum accuracy)
3. High-quality satellite tiles cached (Zoom level 19, ~0.30 m/pixel)
4. Ground truth GPS coordinates available
5. Test dataset prepared: Test_Baseline (AD000001-AD000030)

Test Description

Process same baseline flight as AC-1 test, but now validate the more stringent criterion that at least 60% of images achieve error < 20 meters. This tests the precision of LiteSAM cross-view matching.

Test Steps

Step 1: Initialize System for High Precision

• Action: Start system, verify models loaded in optimal configuration
• Expected Result: System ready, LiteSAM configured for maximum precision

Step 2: Create Test Flight

• Action: Create flight "AC2_HighPrecision" with same parameters as AC-1
• Expected Result: Flight created successfully

Step 3: Upload Test Images

• Action: Upload AD000001-AD000030 (30 images)
• Expected Result: All queued for processing

Step 4: Process with High-Quality Anchors

• Action: System processes images, L3 provides frequent GPS anchors
• Expected Result:
  • Processing completes
  • Multiple GPS anchors per 10 images
  • Factor graph well-constrained

Step 5: Retrieve Final Results

• Action: GET /flights/{flightId}/results?include_refined=true
• Expected Result: Refined GPS coordinates (post-optimization)

Step 6: Calculate Errors

• Action: Calculate haversine distance for each image
• Expected Result: Error array with 30 values

Step 7: Validate AC-2

• Action: Count images with error < 20m, calculate percentage
• Expected Result: ≥ 60% of images have error < 20 meters ✓

Step 8: Analyze High-Precision Results

• Action: Identify which images achieve < 20m vs 20-50m vs > 50m
• Expected Result:
  • Category 1 (< 20m): ≥ 18 images (60%)
  • Category 2 (20-50m): ~10 images
  • Category 3 (> 50m): < 2 images

Step 9: Generate Detailed Report

• Action: Create comprehensive accuracy report
• Expected Result:
  • Percentage breakdown by error thresholds
  • Distribution histogram
  • Correlation between accuracy and image features
  • Compliance matrix for AC-1 and AC-2

Success Criteria

Primary Criterion (AC-2):

• ≥ 18 out of 30 images (60%) have GPS error < 20 meters

Supporting Criteria:

• Also meets AC-1 (≥ 80% < 50m)
• Mean error < 30 meters
• RMSE < 35 meters
• No catastrophic failures (errors > 200m)

Expected Results

Total Images: 30
Successfully Processed: 30 (100%)
Images with error < 10m: 8 (26.7%)
Images with error < 20m: 20 (66.7%)
Images with error < 50m: 28 (93.3%)
Images with error > 50m: 2 (6.7%)
Mean Error: 24.5m
Median Error: 18.2m
RMSE: 28.3m
90th Percentile: 42.1m
AC-2 Status: PASS (66.7% > 60%)
AC-1 Status: PASS (93.3% > 80%)

Pass/Fail Criteria

TEST PASSES IF:

• ≥ 60% of images achieve error < 20m
• Also passes AC-1 (≥ 80% < 50m)
• System performance stable across multiple runs

TEST FAILS IF:

• < 60% of images achieve error < 20m
• Fails AC-1 (would be critical failure)
• Results not reproducible (high variance)

Error Analysis

If test fails or borderline:

Investigate:

1. Satellite Data Quality: Check zoom level, age of imagery, resolution
2. LiteSAM Performance: Review correspondence counts, homography quality
3. Factor Graph: Check if GPS anchors frequent enough
4. Image Quality: Verify no motion blur, good lighting conditions
5. Altitude Variation: Check if altitude assumption (400m) accurate

Potential Improvements:

• Use Tier-2 commercial satellite data (higher resolution)
• Increase GPS anchor frequency (every 3rd image vs every 5th)
• Tune LiteSAM confidence threshold
• Apply per-keyframe scale adjustment in factor graph

Notes

• AC-2 is more stringent than AC-1 (20m vs 50m)
• Achieving 60% at 20m while maintaining 80% at 50m validates solution design
• LiteSAM reported RMSE of 17.86m on UAV-VisLoc dataset supports feasibility
• Test represents high-precision navigation requirement