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Added Perplexity 01_solution_draft
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Denys Zaitsev
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# Testing Strategy & Acceptance Test Plan (ATP)
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## Overview
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This document details the comprehensive testing strategy for the UAV Aerial Image Geolocalization System, covering unit tests, integration tests, system tests, field validation, and acceptance criteria.
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---
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## 1. Test Pyramid Architecture
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```
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▲
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/|\
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/ | \
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ACCEPTANCE TESTS (5%)
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┌─────────────────────────┐
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/│ Field Trials │\
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/ │ Real UAV Flights │ \
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/ └─────────────────────────┘ \
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╱ ╲
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SYSTEM TESTS (15%)
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┌────────────────────────────────┐
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/│ End-to-End Performance │\
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/ │ Accuracy, Speed, Robustness │ \
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/ └────────────────────────────────┘ \
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╱ ╲
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INTEGRATION TESTS (30%)
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┌─────────────────────────────────┐
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/│ Multi-Component Workflows │\
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/ │ FeatureMatcher → Triangulator │ \
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│ Bundle Adjustment Refinement │
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└─────────────────────────────────┘
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UNIT TESTS (50%)
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┌─────────────────────────┐
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/│ Individual Components │\
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/ │ AKAZE, Essential Matrix │
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│ Triangulation, BA... │
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└─────────────────────────┘
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```
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---
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## 2. Detailed Test Categories
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### 2.1 Unit Tests (Level 1)
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#### UT-1: Feature Extraction (AKAZE)
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```
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Purpose: Verify keypoint detection and descriptor computation
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Test Data: Synthetic images with known features (checkerboard patterns)
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Test Cases:
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├─ UT-1.1: Basic feature detection
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│ Input: 1024×768 synthetic image with checkerboard
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│ Expected: ≥500 keypoints detected
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│ Pass: count ≥ 500
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│
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├─ UT-1.2: Scale invariance
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│ Input: Same scene at 2x scale
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│ Expected: Keypoints at proportional positions
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│ Pass: correlation of positions > 0.9
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│
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├─ UT-1.3: Rotation robustness
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│ Input: Image rotated ±30°
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│ Expected: Descriptors match original + rotated
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│ Pass: match rate > 80%
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│
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├─ UT-1.4: Multi-scale handling
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│ Input: Image with features at multiple scales
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│ Expected: Features detected at all scales (pyramid)
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│ Pass: ratio of scales [1:1.2:1.44:...] verified
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│
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└─ UT-1.5: Performance constraint
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Input: FullHD image (1920×1080)
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Expected: <500ms feature extraction
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Pass: 95th percentile < 500ms
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```
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#### UT-2: Feature Matching
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```
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Purpose: Verify robust feature correspondence
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Test Data: Pairs of synthetic/real images with known correspondence
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Test Cases:
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├─ UT-2.1: Basic matching
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│ Input: Two images from synthetic scene (90% overlap)
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│ Expected: ≥95% of ground-truth features matched
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│ Pass: match_rate ≥ 0.95
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│
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├─ UT-2.2: Outlier rejection (Lowe's ratio test)
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│ Input: Synthetic pair + 50% false features
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│ Expected: False matches rejected
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│ Pass: false_match_rate < 0.1
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│
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├─ UT-2.3: Low overlap scenario
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│ Input: Two images with 20% overlap
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│ Expected: Still matches ≥20 points
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│ Pass: min_matches ≥ 20
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│
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└─ UT-2.4: Performance
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Input: FullHD images, 1000 features each
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Expected: <300ms matching time
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Pass: 95th percentile < 300ms
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```
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#### UT-3: Essential Matrix Estimation
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```
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Purpose: Verify 5-point/8-point algorithms for camera geometry
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Test Data: Synthetic correspondences with known relative pose
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Test Cases:
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├─ UT-3.1: 8-point algorithm
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│ Input: 8+ point correspondences
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│ Expected: Essential matrix E with rank 2
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│ Pass: min_singular_value(E) < 1e-6
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│
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├─ UT-3.2: 5-point algorithm
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│ Input: 5 point correspondences
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│ Expected: Up to 4 solutions generated
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│ Pass: num_solutions ∈ [1, 4]
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│
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├─ UT-3.3: RANSAC convergence
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│ Input: 100 correspondences, 30% outliers
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│ Expected: Essential matrix recovery despite outliers
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│ Pass: inlier_ratio ≥ 0.6
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│
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└─ UT-3.4: Chirality constraint
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Input: Multiple (R,t) solutions from decomposition
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Expected: Only solution with points in front of cameras selected
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Pass: selected_solution verified via triangulation
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```
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#### UT-4: Triangulation (DLT)
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```
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Purpose: Verify 3D point reconstruction from image correspondences
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Test Data: Synthetic scenes with known 3D geometry
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Test Cases:
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├─ UT-4.1: Accuracy
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│ Input: Noise-free point correspondences
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│ Expected: Reconstructed X matches ground truth
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│ Pass: RMSE < 0.1cm on 1m scene
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│
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├─ UT-4.2: Outlier handling
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│ Input: 10 valid + 2 invalid correspondences
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│ Expected: Invalid points detected (behind camera/far)
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│ Pass: valid_mask accuracy > 95%
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│
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├─ UT-4.3: Altitude constraint
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│ Input: Points with z < 50m (below aircraft)
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│ Expected: Points rejected
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│ Pass: altitude_filter works correctly
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│
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└─ UT-4.4: Batch performance
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Input: 500 point triangulations
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Expected: <100ms total
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Pass: 95th percentile < 100ms
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```
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#### UT-5: Bundle Adjustment
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```
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Purpose: Verify pose and 3D point optimization
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Test Data: Synthetic multi-view scenes
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Test Cases:
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├─ UT-5.1: Convergence
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│ Input: 5 frames with noisy initial poses
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│ Expected: Residual decreases monotonically
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│ Pass: final_residual < 0.001 * initial_residual
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│
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├─ UT-5.2: Covariance computation
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│ Input: Optimized poses and points
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│ Expected: Covariance matrix positive-definite
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│ Pass: all_eigenvalues > 0
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│
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├─ UT-5.3: Window size effect
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│ Input: Same problem with window sizes [3, 5, 10]
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│ Expected: Larger windows → better residuals
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│ Pass: residual_5 < residual_3, residual_10 < residual_5
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│
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└─ UT-5.4: Performance scaling
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Input: Window size [5, 10, 15, 20]
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Expected: Time ~= O(w^3)
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Pass: quadratic fit accurate (R² > 0.95)
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```
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---
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### 2.2 Integration Tests (Level 2)
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#### IT-1: Sequential Pipeline
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```
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Purpose: Verify image-to-image processing chain
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Test Data: Real aerial image sequences (5-20 images)
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Test Cases:
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├─ IT-1.1: Feature flow
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│ Features extracted from img₁ → tracked to img₂ → matched
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│ Expected: Consistent tracking across images
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│ Pass: ≥70% features tracked end-to-end
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│
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├─ IT-1.2: Pose chain consistency
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│ Poses P₁, P₂, P₃ computed sequentially
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│ Expected: P₃ = P₂ ∘ P₂₋₁ (composition consistency)
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│ Pass: pose_error < 0.1° rotation, 5cm translation
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│
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├─ IT-1.3: Trajectory smoothness
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│ Velocity computed between poses
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│ Expected: Smooth velocity profile (no jumps)
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│ Pass: velocity_std_dev < 20% mean_velocity
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│
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└─ IT-1.4: Memory usage
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Process 100-image sequence
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Expected: Constant memory (windowed processing)
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Pass: peak_memory < 2GB
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```
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#### IT-2: Satellite Georeferencing
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```
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Purpose: Verify local-to-global coordinate transformation
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Test Data: Synthetic/real images with known satellite reference
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Test Cases:
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├─ IT-2.1: Feature matching with satellite
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│ Input: Aerial image + satellite reference
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│ Expected: ≥10 matched features between viewpoints
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│ Pass: match_count ≥ 10
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│
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├─ IT-2.2: Homography estimation
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│ Matched features → homography matrix
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│ Expected: Valid transformation (3×3 matrix)
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│ Pass: det(H) ≠ 0, condition_number < 100
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│
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├─ IT-2.3: GPS transformation accuracy
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│ Apply homography to image corners
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│ Expected: Computed GPS ≈ known reference GPS
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│ Pass: error < 100m (on test data)
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│
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└─ IT-2.4: Confidence scoring
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Compute inlier_ratio and MI (mutual information)
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Expected: score = inlier_ratio × MI ∈ [0, 1]
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Pass: high_confidence for obvious matches
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```
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#### IT-3: Outlier Detection Chain
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```
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Purpose: Verify multi-stage outlier detection
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Test Data: Synthetic trajectory with injected outliers
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Test Cases:
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├─ IT-3.1: Velocity anomaly detection
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│ Inject 350m jump at frame N
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│ Expected: Detected as outlier
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│ Pass: outlier_flag = True
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│
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├─ IT-3.2: Recovery mechanism
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│ After outlier detection
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│ Expected: System attempts skip-frame matching (N→N+2)
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│ Pass: recovery_successful = True
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│
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├─ IT-3.3: False positive rate
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│ Normal sequence with small perturbations
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│ Expected: <5% false outlier flagging
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│ Pass: false_positive_rate < 0.05
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│
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└─ IT-3.4: Consistency across stages
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Multiple detection stages should agree
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Pass: agreement_score > 0.8
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```
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---
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### 2.3 System Tests (Level 3)
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#### ST-1: Accuracy Criteria
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```
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Purpose: Verify system meets ±50m and ±20m accuracy targets
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Test Data: Real aerial image sequences with ground-truth GPS
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Test Cases:
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├─ ST-1.1: 50m accuracy target
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│ Input: 500-image flight
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│ Compute: % images within 50m of ground truth
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│ Expected: ≥80%
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│ Pass: accuracy_50m ≥ 0.80
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│
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├─ ST-1.2: 20m accuracy target
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│ Same flight data
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│ Expected: ≥60% within 20m
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│ Pass: accuracy_20m ≥ 0.60
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│
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├─ ST-1.3: Mean absolute error
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│ Compute: MAE over all images
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│ Expected: <40m typical
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│ Pass: MAE < 50m
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│
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└─ ST-1.4: Error distribution
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Expected: Error approximately Gaussian
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Pass: K-S test p-value > 0.05
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```
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#### ST-2: Registration Rate
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```
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Purpose: Verify ≥95% of images successfully registered
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Test Data: Real flights with various conditions
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Test Cases:
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├─ ST-2.1: Baseline registration
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│ Good overlap, clear features
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│ Expected: >98% registration rate
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│ Pass: registration_rate ≥ 0.98
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│
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├─ ST-2.2: Challenging conditions
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│ Low texture, variable lighting
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│ Expected: ≥95% registration rate
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│ Pass: registration_rate ≥ 0.95
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│
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├─ ST-2.3: Sharp turns scenario
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│ Images with <10% overlap
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│ Expected: Fallback mechanisms trigger, ≥90% success
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│ Pass: fallback_success_rate ≥ 0.90
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│
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└─ ST-2.4: Consecutive failures
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Track max consecutive unregistered images
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Expected: <3 consecutive failures
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Pass: max_consecutive_failures ≤ 3
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```
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#### ST-3: Reprojection Error
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```
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Purpose: Verify <1.0 pixel mean reprojection error
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Test Data: Real flight data after bundle adjustment
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Test Cases:
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├─ ST-3.1: Mean reprojection error
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│ After BA optimization
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│ Expected: <1.0 pixel
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│ Pass: mean_reproj_error < 1.0
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│
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├─ ST-3.2: Error distribution
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│ Histogram of per-point errors
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│ Expected: Tightly concentrated <2 pixels
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│ Pass: 95th_percentile < 2.0 px
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│
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├─ ST-3.3: Per-frame consistency
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│ Error should not vary dramatically
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│ Expected: Consistent across frames
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│ Pass: frame_error_std_dev < 0.3 px
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│
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└─ ST-3.4: Outlier points
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Very large reprojection errors
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Expected: <1% of points with error >3 px
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Pass: outlier_rate < 0.01
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```
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#### ST-4: Processing Speed
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```
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Purpose: Verify <2 seconds per image
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Test Data: Full flight sequences on target hardware
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Test Cases:
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├─ ST-4.1: Average latency
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│ Mean processing time per image
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│ Expected: <2 seconds
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│ Pass: mean_latency < 2.0 sec
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│
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├─ ST-4.2: 95th percentile latency
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│ Worst-case images (complex scenes)
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│ Expected: <2.5 seconds
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│ Pass: p95_latency < 2.5 sec
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│
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├─ ST-4.3: Component breakdown
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│ Feature extraction: <0.5s
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│ Matching: <0.3s
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│ RANSAC: <0.2s
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│ BA: <0.8s
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│ Satellite: <0.3s
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│ Pass: Each component within budget
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│
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└─ ST-4.4: Scaling with problem size
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Memory usage, CPU usage vs. image resolution
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Expected: Linear scaling
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Pass: O(n) complexity verified
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```
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#### ST-5: Robustness - Outlier Handling
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```
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Purpose: Verify graceful handling of 350m outlier drifts
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Test Data: Synthetic/real data with injected outliers
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Test Cases:
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├─ ST-5.1: Single 350m outlier
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│ Inject outlier at frame N
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│ Expected: Detected, trajectory continues
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│ Pass: system_continues = True
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│
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├─ ST-5.2: Multiple outliers
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│ 3-5 outliers scattered in sequence
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│ Expected: All detected, recovery attempted
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│ Pass: detection_rate ≥ 0.8
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│
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├─ ST-5.3: False positive rate
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│ Normal trajectory, no outliers
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│ Expected: <5% false flagging
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│ Pass: false_positive_rate < 0.05
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│
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└─ ST-5.4: Recovery latency
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Time to recover after outlier
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Expected: ≤3 frames
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Pass: recovery_latency ≤ 3 frames
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```
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#### ST-6: Robustness - Sharp Turns
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```
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Purpose: Verify handling of <5% image overlap scenarios
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Test Data: Synthetic sequences with sharp angles
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Test Cases:
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├─ ST-6.1: 5% overlap matching
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│ Two images with 5% overlap
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│ Expected: Minimal matches or skip-frame
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│ Pass: system_handles_gracefully = True
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│
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||||
├─ ST-6.2: Skip-frame fallback
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│ Direct N→N+1 fails, tries N→N+2
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│ Expected: Succeeds with N→N+2
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│ Pass: skip_frame_success_rate ≥ 0.8
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│
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├─ ST-6.3: 90° turn handling
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│ Images at near-orthogonal angles
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│ Expected: Degeneracy detected, logged
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│ Pass: degeneracy_detection = True
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│
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└─ ST-6.4: Trajectory consistency
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Consecutive turns: check velocity smoothness
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Expected: No velocity jumps > 50%
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Pass: velocity_consistency verified
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```
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||||
---
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||||
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||||
### 2.4 Field Acceptance Tests (Level 4)
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||||
|
||||
#### FAT-1: Real UAV Flight Trial #1 (Baseline)
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||||
```
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Scenario: Nominal flight over agricultural field
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┌────────────────────────────────────────┐
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│ Conditions: │
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│ • Clear weather, good sunlight │
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│ • Flat terrain, sparse trees │
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||||
│ • 300m altitude, 50m/s speed │
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│ • 800 images, ~15 min flight │
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└────────────────────────────────────────┘
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||||
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||||
Pass Criteria:
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||||
✓ Accuracy: ≥80% within 50m
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✓ Accuracy: ≥60% within 20m
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||||
✓ Registration rate: ≥95%
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||||
✓ Processing time: <2s/image
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✓ Satellite validation: <10% outliers
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✓ Reprojection error: <1.0px mean
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Success Metrics:
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• MAE (mean absolute error): <40m
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• RMS error: <45m
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• Max error: <200m
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• Trajectory coherence: smooth (no jumps)
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||||
```
|
||||
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||||
#### FAT-2: Real UAV Flight Trial #2 (Challenging)
|
||||
```
|
||||
Scenario: Flight with more complex terrain
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||||
┌────────────────────────────────────────┐
|
||||
│ Conditions: │
|
||||
│ • Mixed urban/agricultural │
|
||||
│ • Buildings, vegetation, water bodies │
|
||||
│ • Variable altitude (250-400m) │
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||||
│ • Includes 1-2 sharp turns │
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||||
│ • 1200 images, ~25 min flight │
|
||||
└────────────────────────────────────────┘
|
||||
|
||||
Pass Criteria:
|
||||
✓ Accuracy: ≥75% within 50m (relaxed from 80%)
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||||
✓ Accuracy: ≥50% within 20m (relaxed from 60%)
|
||||
✓ Registration rate: ≥92% (relaxed from 95%)
|
||||
✓ Processing time: <2.5s/image avg
|
||||
✓ Outliers detected: <15% (relaxed from 10%)
|
||||
|
||||
Fallback Validation:
|
||||
✓ User corrected <20% of uncertain images
|
||||
✓ After correction, accuracy meets FAT-1 targets
|
||||
```
|
||||
|
||||
#### FAT-3: Real UAV Flight Trial #3 (Edge Case)
|
||||
```
|
||||
Scenario: Low-texture flight (challenging for features)
|
||||
┌────────────────────────────────────────┐
|
||||
│ Conditions: │
|
||||
│ • Sandy/desert terrain or water │
|
||||
│ • Minimal features │
|
||||
│ • Overcast/variable lighting │
|
||||
│ • 500-600 images, ~12 min flight │
|
||||
└────────────────────────────────────────┘
|
||||
|
||||
Pass Criteria:
|
||||
✓ System continues (no crash): YES
|
||||
✓ Graceful degradation: Flags uncertainty
|
||||
✓ User can correct and improve: YES
|
||||
✓ Satellite anchor helps recovery: YES
|
||||
|
||||
Success Metrics:
|
||||
• >80% of images tagged "uncertain"
|
||||
• After user correction: meets standard targets
|
||||
• Demonstrates fallback mechanisms working
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. Test Environment Setup
|
||||
|
||||
### Hardware Requirements
|
||||
```
|
||||
CPU: 16+ cores (Intel Xeon / AMD Ryzen)
|
||||
RAM: 64GB minimum (32GB acceptable for <1500 images)
|
||||
Storage: 1TB SSD (for raw images + processing)
|
||||
GPU: Optional (CUDA 11.8+ for 5-10x acceleration)
|
||||
Network: For satellite API queries (can be cached)
|
||||
```
|
||||
|
||||
### Software Requirements
|
||||
```
|
||||
OS: Ubuntu 20.04 LTS or macOS 12+
|
||||
Build: CMake 3.20+, GCC 9+ or Clang 11+
|
||||
Dependencies: OpenCV 4.8+, Eigen 3.4+, GDAL 3.0+
|
||||
Testing: GoogleTest, Pytest
|
||||
CI/CD: GitHub Actions or Jenkins
|
||||
```
|
||||
|
||||
### Test Data Management
|
||||
```
|
||||
Synthetic Data: Generated via Blender (checked into repo)
|
||||
Real Data: External dataset storage (S3/local SSD)
|
||||
Ground Truth: Maintained in CSV format with metadata
|
||||
Versioning: Git-LFS for binary image data
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. Test Execution Plan
|
||||
|
||||
### Phase 1: Unit Testing (Weeks 1-6)
|
||||
```
|
||||
Sprint 1-2: UT-1 (Feature detection) - 2 week
|
||||
Sprint 3-4: UT-2 (Feature matching) - 2 weeks
|
||||
Sprint 5-6: UT-3, UT-4, UT-5 (Geometry) - 2 weeks
|
||||
|
||||
Continuous: Run full unit test suite every commit
|
||||
Coverage target: >90% code coverage
|
||||
```
|
||||
|
||||
### Phase 2: Integration Testing (Weeks 7-12)
|
||||
```
|
||||
Sprint 7-9: IT-1 (Sequential pipeline) - 3 weeks
|
||||
Sprint 10-11: IT-2, IT-3 (Georef, Outliers) - 2 weeks
|
||||
Sprint 12: System integration - 1 week
|
||||
|
||||
Continuous: Integration tests run nightly
|
||||
```
|
||||
|
||||
### Phase 3: System Testing (Weeks 13-18)
|
||||
```
|
||||
Sprint 13-14: ST-1, ST-2 (Accuracy, Registration) - 2 weeks
|
||||
Sprint 15-16: ST-3, ST-4 (Error, Speed) - 2 weeks
|
||||
Sprint 17-18: ST-5, ST-6 (Robustness) - 2 weeks
|
||||
|
||||
Load testing: 1000-3000 image sequences
|
||||
Stress testing: Edge cases, memory limits
|
||||
```
|
||||
|
||||
### Phase 4: Field Acceptance (Weeks 19-30)
|
||||
```
|
||||
Week 19-22: FAT-1 (Baseline trial)
|
||||
• Coordinate 1-2 baseline flights
|
||||
• Validate system on real data
|
||||
• Adjust parameters as needed
|
||||
|
||||
Week 23-26: FAT-2 (Challenging trial)
|
||||
• More complex scenarios
|
||||
• Test fallback mechanisms
|
||||
• Refine user interface
|
||||
|
||||
Week 27-30: FAT-3 (Edge case trial)
|
||||
• Low-texture scenarios
|
||||
• Validate robustness
|
||||
• Final adjustments
|
||||
|
||||
Post-trial: Generate comprehensive report
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. Acceptance Criteria Summary
|
||||
|
||||
| Criterion | Target | Test | Pass/Fail |
|
||||
|-----------|--------|------|-----------|
|
||||
| **Accuracy@50m** | ≥80% | FAT-1 | ≥80% pass |
|
||||
| **Accuracy@20m** | ≥60% | FAT-1 | ≥60% pass |
|
||||
| **Registration Rate** | ≥95% | ST-2 | ≥95% pass |
|
||||
| **Reprojection Error** | <1.0px mean | ST-3 | <1.0px pass |
|
||||
| **Processing Speed** | <2.0s/image | ST-4 | p95<2.5s pass |
|
||||
| **Robustness (350m outlier)** | Handled | ST-5 | Continue pass |
|
||||
| **Sharp turns (<5% overlap)** | Handled | ST-6 | Skip-frame pass |
|
||||
| **Satellite validation** | <10% outliers | FAT-1-3 | <10% pass |
|
||||
|
||||
---
|
||||
|
||||
## 6. Success Metrics
|
||||
|
||||
**Green Light Criteria** (Ready for production):
|
||||
- ✅ All unit tests pass (100%)
|
||||
- ✅ All integration tests pass (100%)
|
||||
- ✅ All system tests pass (100%)
|
||||
- ✅ FAT-1 and FAT-2 pass acceptance criteria
|
||||
- ✅ FAT-3 shows graceful degradation
|
||||
- ✅ <10% code defects discovered in field trials
|
||||
- ✅ Performance meets SLA consistently
|
||||
|
||||
**Yellow Light Criteria** (Conditional deployment):
|
||||
- ⚠ 85-89% of acceptance criteria met
|
||||
- ⚠ Minor issues in edge cases
|
||||
- ⚠ Requires workaround documentation
|
||||
- ⚠ Re-test after fixes
|
||||
|
||||
**Red Light Criteria** (Do not deploy):
|
||||
- ❌ <85% of acceptance criteria met
|
||||
- ❌ Critical failures in core functionality
|
||||
- ❌ Safety/security concerns
|
||||
- ❌ Cannot meet latency or accuracy targets
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
This comprehensive testing strategy ensures the UAV Image Geolocalization System is robust, accurate, and reliable before production deployment. The multi-level approach (unit → integration → system → field) progressively validates system behavior from components to end-to-end scenarios, with field trials providing real-world validation of the acceptance criteria.
|
||||
Reference in New Issue
Block a user