EdgeSentry-Inspect — Architecture

Edge-cloud split

┌──────────────────────────────────────────────────────────┐
│  FIELD PC (Edge)                                         │
│                                                          │
│  3D sensor (LiDAR / ToF)                                 │
│      │  point cloud (PointCloud)                         │
│      ▼                                                   │
│  trilink-core::project_to_depth_map                      │
│  trilink-core::project_to_height_map                     │
│      │  DepthMap  HeightMap                              │
│      ▼                                                   │
│  AI inference (built-in model or HTTP endpoint)         │
│      │  Vec<Detection>  (BBox2D + class + confidence)    │
│      ▼                                                   │
│  trilink-core::unproject                                 │
│      │  world-space Point3D per detection                │
│      ▼                                                   │
│  edgesentry-inspect::ifc      — IFC geometry             │
│  edgesentry-inspect::deviation — deviation (mm)          │
│  edgesentry-inspect::heatmap   — heatmap PNG             │
│  edgesentry-inspect::report    — JSON report             │
│      │                                                   │
│      ├── displayed on tablet / AR headset immediately    │
└──────┬───────────────────────────────────────────────────┘
       │  report JSON + heatmap PNG  (not raw point cloud)
       ▼
┌──────────────────────────────────────────────────────────┐
│  CLOUD (Audit Store / Digital Twin)                      │
│                                                          │
│  edgesentry-inspect::sync                                │
│      │  S3-compatible upload (Object Lock WORM)          │
│      │  structural-change flag → message queue           │
│      ▼                                                   │
│  Audit report store   — immutable evidence               │
│  Digital twin update  — as-built IFC delta               │
│  Central dashboard    — fleet-wide deviation trends      │
└──────────────────────────────────────────────────────────┘

What runs on the field PC

What goes to the cloud

Component design

edgesentry-inspect::ifc

• Input: IFC file path (.ifc)
• Output: Vec<Point3D> — design reference point cloud sampled from wall/slab/column geometry
• Implementation: ifcopenshell via Python FFI (pyo3) or a native Rust IFC reader
• The reference cloud is loaded once per inspection session and cached in memory

edgesentry-inspect::deviation

• Input: scan Vec<Point3D> (from trilink-core::unproject) + design Vec<Point3D> (from ifc)
• Output: per-scan-point deviation f32 in metres
• Algorithm: k-d tree nearest-neighbour search (kiddo crate); O(n log m) per scan
• Threshold: configurable (default 10 mm for construction, 5 mm for maritime hull)

edgesentry-inspect::heatmap

• Input: scan points + per-point deviation values
• Output: PNG image — deviation mapped to colour (green ≤ threshold, yellow 2×, red 4×+)
• Reuses trilink-core::project_to_depth_map to position coloured points in 2D

edgesentry-inspect::report

JSON schema:

{
  "compliant_pct": 94.2,
  "max_deviation_mm": 23.1,
  "mean_deviation_mm": 3.8,
  "point_count": 142850,
  "threshold_mm": 10.0
}

AI detection locations are written to points.json alongside the report:

{
  "scan_points": [
    { "x": 12.3, "y": 4.1, "z": 2.05, "deviation_mm": 23.1 }
  ],
  "detections": [
    { "x": 12.3, "y": 4.1, "z": 2.05 }
  ]
}

edgesentry-inspect::sync

• Uploads report JSON and heatmap PNG to an S3-compatible audit store (Object Lock WORM)
• Emits a structural-change flag to a message queue (SQS or MQTT) when any anomaly exceeds 2× the configured threshold
• Reuses the S3-compatible interface pattern from edgesentry-rs

AI inference modes

EdgeSentry-Inspect supports two inference backends, selected by inference.mode in config.toml. Both produce the same Vec<Detection> output consumed by the rest of the pipeline.

Built-in model (inference.mode = "builtin")

A lightweight defect-detection model bundled with EdgeSentry-Inspect. Runs in-process via ONNX Runtime — no external server or network access required.

• Input: DepthMap + HeightMap images produced by trilink-core
• Output: Vec<Detection> — bounding boxes with class labels and confidence scores
• Initial class coverage: surface_void, misalignment, rebar_exposure
• Hardware: runs on a standard field PC CPU; no dedicated GPU required for basic use

Use builtin for getting started quickly, offline-only deployments, or when no vendor model is available.

External HTTP endpoint (inference.mode = "http")

The inference client POSTs the depth map and height map to inference.base_url and receives a detection list. The endpoint can be:

• A vendor’s model server running locally on the field PC or robot (same host, no internet needed)
• A specialised cloud inference API (Scenario 1 / connected deployments only)

This mode is the integration point for vendor collaboration. Vendors implement the server side with their own model; EdgeSentry-Inspect calls it with a fixed schema. The operator sets inference.base_url in config — no code change required.

Interface contract:

POST /detect
Content-Type: multipart/form-data
  depth_map: <PNG bytes>
  height_map: <PNG bytes>

200 OK
[{"x":120,"y":45,"w":30,"h":20,"class":"surface_void","confidence":0.87}, ...]

Optional: cryptographically verifiable audit records

If the inspection context requires mathematically verifiable, tamper-evident audit records — for example, regulatory submissions where a third party must independently verify that a report was not altered after the fact — the deviation report can be signed and hash-chained using edgesentry-rs.

edgesentry-rs provides:

This layer is opt-in. For standard construction inspections, the S3 Object Lock WORM store (edgesentry-inspect::sync) is sufficient. For high-assurance contexts (maritime hull certification, legally binding structural sign-off), wrapping the report in an edgesentry-rs AuditRecord before upload provides a cryptographic audit trail that can be verified independently of EdgeSentry-Inspect infrastructure.

Accuracy factors

Target accuracy is 10 mm for construction (UC-1) and 5 mm for maritime (UC-2). The following table shows the main factors that determine measurement accuracy in the field and how each is mitigated.

Technology summary

Open datasets for PoC