gps-denied-desktop/docs/03_tests/38_outlier_anchor_detection_spec.md

Acceptance Test: Outlier Anchor Detection (<10%)

Summary

Validate AC-5 requirement that the system detects and rejects outlier global anchor points (bad satellite matches from L3) keeping outlier rate below 10%.

Linked Acceptance Criteria

AC-5: Less than 10% outlier anchors. The system can tolerate some incorrect global anchor points (bad satellite matches) but must keep them below 10% threshold through validation and rejection mechanisms.

Preconditions

• ASTRAL-Next system operational
• L3 LiteSAM cross-view matching active
• Factor graph with robust M-estimation
• Validation mechanisms enabled (geometric consistency, residual analysis)

Test Data

• Dataset: AD000001-AD000060 (60 images)
• Expected Anchors: ~20-30 global anchor attempts (not every frame needs L3)
• Acceptable Outliers: <3 outlier anchors (<10% of 30)
• Challenge: Potential satellite data staleness, seasonal differences

Test Steps

Step 1: Process Flight with L3 Anchoring

Action: Process full flight with L3 metric refinement active Expected Result:

• L2 retrieves satellite tiles for keyframes
• L3 LiteSAM performs cross-view matching
• Global anchor factors added to factor graph
• Anchor count: 20-30 across 60 images
• Status: PROCESSING_WITH_ANCHORS

Step 2: Monitor Anchor Quality Metrics

Action: Track L3 matching confidence and geometric consistency Expected Result:

• Each anchor has confidence score (0-1)
• Each anchor has initial residual error
• Anchors with confidence <0.3 flagged as suspicious
• Anchors with residual >3σ flagged as outliers
• Status: MONITORING_QUALITY

Step 3: Identify Potential Outlier Anchors

Action: Analyze anchors that conflict with trajectory consensus Expected Result:

Total anchors: 25 (example)
High confidence (>0.7): 20
Medium confidence (0.4-0.7): 3
Low confidence (<0.4): 2
Flagged as outliers: 2 (<10%)

Step 4: Validate Outlier Rejection Mechanism

Action: Verify factor graph handling of outlier anchors Expected Result:

• Outlier anchors automatically down-weighted by robust kernel
• Outlier anchor residuals remain high (not dragging trajectory)
• Non-outlier anchors maintain weight ~1.0
• Factor graph converges despite outlier anchors present
• Status: OUTLIERS_HANDLED

Step 5: Test Explicit Outlier Anchor Scenario

Action: Manually inject known bad anchor (simulated wrong satellite tile match) Expected Result:

• Bad anchor creates large residual (>100m error)
• Geometric validation detects inconsistency
• Robust cost function down-weights bad anchor
• Bad anchor does NOT corrupt trajectory
• Status: SYNTHETIC_OUTLIER_REJECTED

Step 6: Calculate Final Anchor Statistics

Action: Analyze all anchor attempts and outcomes Expected Result:

Total anchor attempts: 25-30
Successful anchors: 23-27 (90-95%)
Outlier anchors: 2-3 (<10%)
Outlier detection rate: 100% (all caught)
False positive rate: <5% (good anchors not rejected)
Trajectory accuracy: Improved by valid anchors
AC-5 Status: PASS

Pass/Fail Criteria

PASS if:

• Outlier anchor rate <10% of total anchor attempts
• All significant outliers (>100m error) detected and down-weighted
• Factor graph converges with MRE <1.5px
• Valid anchors improve trajectory accuracy vs L1-only
• No trajectory corruption from outlier anchors

FAIL if:

• Outlier anchor rate >10%

• 1 outlier anchor corrupts trajectory (causes >50m error propagation)

• Outlier detection fails (outliers not flagged)
• Factor graph diverges due to conflicting anchors
• Valid anchors incorrectly rejected (>10% false positive rate)

Outlier Detection Mechanisms Tested

Geometric Consistency Check

• Compare anchor position with L1 trajectory estimate
• Flag if discrepancy >100m

Residual Analysis

• Monitor residual error in factor graph optimization
• Flag if residual >3σ from mean anchor residual

Confidence Thresholding

• L3 LiteSAM outputs matching confidence
• Reject anchors with confidence <0.2

Robust M-Estimation

• Cauchy/Huber kernel automatically down-weights high-residual anchors
• Prevents outliers from corrupting optimization

Technical Validation Metrics

• Anchor Attempt Rate: 30-50% of frames (keyframes only)
• Anchor Success Rate: 90-95%
• Outlier Rate: <10% (AC-5 requirement)
• Detection Sensitivity: >95% (outliers caught)
• Detection Specificity: >90% (valid anchors retained)

Failure Modes Tested

• Wrong Satellite Tile: L2 retrieves incorrect location
• Stale Satellite Data: Terrain changed significantly
• Seasonal Mismatch: Summer satellite vs winter UAV imagery
• Rotation Error: L3 estimates incorrect rotation

Notes

• AC-5 is critical for hybrid localization reliability
• 10% outlier tolerance allows graceful degradation
• Robust M-estimation is the primary outlier defense
• Multiple validation layers provide defense-in-depth
• Valid anchors significantly improve absolute accuracy