# Question Decomposition

## Original Question
"Analyze completeness of the current solution. How mature is it?"

## Active Mode
**Mode B: Solution Assessment** — 5 solution drafts exist (`solution_draft01.md` through `solution_draft05.md`). Assessing the latest draft (05) for completeness and maturity.

## Question Type Classification
**Knowledge Organization + Problem Diagnosis**
- Knowledge Organization: systematically map what a complete GPS-denied nav system requires vs what is present
- Problem Diagnosis: identify gaps, weak points, and missing elements that reduce maturity

## Research Subject Boundary Definition

| Dimension | Boundary |
|-----------|----------|
| **Population** | Fixed-wing UAV GPS-denied visual navigation systems using visual odometry + satellite matching + IMU fusion |
| **Geography** | Eastern/southern Ukraine conflict zone operations |
| **Timeframe** | Current state of art (2024-2026), focusing on Jetson-class embedded deployment |
| **Level** | System-level architecture completeness — from sensor input to flight controller output |

## Problem Context Summary

The solution is a real-time GPS-denied visual navigation system for a custom 3.5m fixed-wing UAV:
- **Hardware**: Jetson Orin Nano Super (8GB), ADTI 20L V1 camera (0.7fps), Viewpro A40 Pro gimbal, Pixhawk 6x
- **Core pipeline**: cuVSLAM VO (0.7fps) → ESKF fusion → GPS_INPUT via pymavlink at 5-10Hz
- **Satellite correction**: LiteSAM/EfficientLoFTR/XFeat TRT FP16 on keyframes, async Stream B
- **5 draft iterations**: progressed from initial architecture → TRT migration → camera rate correction + UAV platform specs
- **Supporting docs**: tech_stack.md, security_analysis.md

## Decomposed Sub-Questions (Mode B)

### Functional Completeness
- **SQ-1**: What components does a mature GPS-denied visual navigation system require that are missing or under-specified in the current draft?
- **SQ-2**: How complete is the ESKF sensor fusion specification? (state vector, process model, measurement models, Q/R tuning, observability analysis)
- **SQ-3**: How does the system handle disconnected route segments (sharp turns with no overlap)? Is this adequately specified?
- **SQ-4**: What coordinate system transformations are needed (camera → body → NED → WGS84) and are they specified?
- **SQ-5**: How does the system handle initial localization (first frame + satellite matching bootstrap)?
- **SQ-6**: Is the re-localization request workflow (to ground station) sufficiently defined?
- **SQ-7**: How complete is the offline tile preparation pipeline (zoom levels, storage requirements, coverage calculation)?
- **SQ-8**: Is the object localization component sufficiently specified for operational use?

### Performance & Robustness
- **SQ-9**: What are the realistic drift characteristics of cuVSLAM at 0.7fps over long straight segments?
- **SQ-10**: How robust is satellite matching with Google Maps imagery in the operational area?
- **SQ-11**: What happens during extended periods with no satellite match (cloud cover on tiles, homogeneous terrain)?
- **SQ-12**: Is the 5-10Hz GPS_INPUT rate adequate for the flight controller's EKF?

### Maturity Assessment
- **SQ-13**: What is the Technology Readiness Level (TRL) of each component?
- **SQ-14**: What validation/testing has been done vs what is only planned?
- **SQ-15**: What operational procedures are missing (pre-flight checklist, in-flight monitoring, post-flight analysis)?
- **SQ-16**: Are there any inconsistencies between documents (tech_stack.md, security_analysis.md, solution_draft05.md)?

### Security
- **SQ-17**: Are there security gaps not covered by the existing security_analysis.md?
- **SQ-18**: How does the MAVLink GPS_INPUT message security work (spoofing of the GPS replacement itself)?

## Timeliness Sensitivity Assessment

- **Research Topic**: GPS-denied visual navigation system completeness and maturity
- **Sensitivity Level**: 🟡 Medium
- **Rationale**: Core algorithms (VO, ESKF, feature matching) are well-established. Hardware (Jetson Orin Nano Super) is relatively new but stable. cuVSLAM library updates are moderate pace. No rapidly-changing AI/LLM dependencies.
- **Source Time Window**: 1-2 years
- **Priority official sources to consult**:
  1. NVIDIA cuVSLAM / Isaac ROS documentation
  2. PX4/ArduPilot MAVLink GPS_INPUT documentation
  3. LiteSAM / EfficientLoFTR papers and repos
- **Key version information to verify**:
  - cuVSLAM: PyCuVSLAM v15.0.0
  - TensorRT: 10.3.0
  - JetPack: 6.2.2
