add tests

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solution.md updated

docs/03_tests/23_vision_to_optimization_pipeline_spec.md

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+# System Integration Test: Vision to Optimization Pipeline
+
+## Summary
+Validate integration between vision layers (L1, L2, L3) and Factor Graph Optimizer.
+
+## Components Under Test
+- Sequential Visual Odometry (L1)
+- Global Place Recognition (L2)
+- Metric Refinement (L3)
+- Factor Graph Optimizer
+- Result Manager
+
+## Test Scenario
+Test the flow of vision estimates into factor graph optimization:
+1. L1 provides relative pose factors
+2. L3 provides absolute GPS factors
+3. Factor graph fuses both into optimized trajectory
+4. Results show improvement over individual layers
+
+## Test Cases
+
+### Test Case 1: Sequential Factors Only (L1)
+- Process AD000001-AD000010 with L1 only
+- Feed relative poses to factor graph
+- Verify: Drift accumulates without GPS anchors
+
+### Test Case 2: GPS Anchors Only (L3)
+- Process same images with L3 only
+- Feed absolute GPS to factor graph
+- Verify: Accurate but no temporal smoothness
+
+### Test Case 3: Fused L1 + L3 (Optimal)
+- Process with both L1 and L3
+- Factor graph fuses relative and absolute factors
+- Verify: Better accuracy than L1-only, smoother than L3-only
+
+### Test Case 4: L2 Recovery after L1 Failure
+- Simulate L1 tracking loss
+- L2 recovers global location
+- L3 refines it
+- Factor graph incorporates recovery
+
+### Test Case 5: Robust Outlier Handling
+- Include outlier measurement (268m jump)
+- Verify robust kernel down-weights outlier
+- Trajectory remains consistent
+
+### Test Case 6: Incremental Updates
+- Add images one by one
+- Factor graph updates incrementally
+- Verify past trajectory refined when new anchors arrive (AC-8)
+
+## Success Criteria
+- L1-only shows drift (errors grow over time)
+- L3-only accurate but may be jagged
+- L1+L3 fusion achieves best results
+- Outliers handled without breaking trajectory
+- Incremental updates work correctly
+- Accuracy improves over single-layer estimates
+
+## Maximum Expected Time
+- L1-only (10 images): < 10 seconds
+- L3-only (10 images): < 15 seconds
+- Fused (10 images): < 20 seconds
+- Total test: < 60 seconds
+
+## Pass/Fail Criteria
+**Passes If**: Factor graph successfully fuses vision estimates, accuracy improved, outliers handled
+**Fails If**: Fusion fails, accuracy worse than single layer, outliers corrupt trajectory
+