feat: add calibration_status_classifier v2.0 artifacts (from awf.ml.dev.web.auto/sensing/models/calibration_status_classifier/v2)

.gitignore

@@ -0,0 +1,5 @@
+# Auto-generated TensorRT artifacts, built locally by Autoware from the ONNX
+# files (see autoware_tensorrt_common). They are environment-specific
+# (GPU arch + TensorRT version) and must not be committed to this repo.
+*.engine
+*.json

README.md

@@ -0,0 +1,115 @@
+---
+license: apache-2.0
+pipeline_tag: image-classification
+tags:
+  - autoware
+  - ros2
+  - autonomous-driving
+  - lidar
+  - camera
+  - calibration
+  - sensing
+  - tensorrt
+  - onnx
+---
+
+# Calibration Status Classifier for Autoware (`calibration_status_classifier`)
+
+Deep learning model for real-time LiDAR-camera calibration validation, used by the
+[`autoware_calibration_status_classifier`](https://github.com/autowarefoundation/autoware_universe/tree/main/sensing/autoware_calibration_status_classifier)
+node in [Autoware](https://github.com/autowarefoundation/autoware).
+
+The node projects the LiDAR point cloud onto the (undistorted) camera image, feeds the resulting
+5-channel tensor to this network, and classifies whether the LiDAR-camera extrinsic calibration is
+still valid. The model is exported as ONNX and runs with TensorRT inside Autoware; Autoware builds
+the TensorRT engine from the ONNX file on first launch.
+
+## Model overview
+
+| | |
+| --- | --- |
+| Task | Binary classification of LiDAR-camera calibration status (calibrated vs. miscalibrated) |
+| Input | 5-channel normalized image tensor: RGB + projected LiDAR depth + intensity |
+| Output | Calibration status with a miscalibration confidence score |
+| Input resolution | Dynamic; TensorRT optimization profile height `[1080, 1860, 2160]`, width `[1920, 2880, 3840]` (min, opt, max) |
+| Runtime | TensorRT (FP16 by default, FP32 selectable) via the `autoware_calibration_status_classifier` ROS 2 node |
+| Format | ONNX (Autoware builds the TensorRT engine locally on first launch) |
+| License | Apache-2.0 |
+
+Pre-processing runs CUDA-accelerated in the node, not in the ONNX graph: image undistortion,
+projection of 3D LiDAR points onto the 2D image plane (adding depth and intensity channels), and
+morphological dilation of the projected points.
+
+## Files
+
+| File | Description |
+| --- | --- |
+| `calibration_status_classifier.onnx` | Calibration status classification network |
+| `ml_package_calibration_status_classifier.param.yaml` | Model parameters (max projection depth, dilation kernel size, input resolution profile) |
+| `deploy_metadata.yaml` | Deployment metadata recording the artifact version of this repository |
+
+> **TensorRT engines are not distributed here.** TensorRT engines are specific to the GPU
+> architecture and TensorRT version they are built on and are not portable, so Autoware builds them
+> locally from the ONNX file on first launch (or via `build_only:=true`).
+
+## Inputs and outputs (as used by the node)
+
+**Inputs**
+
+- LiDAR point cloud topics (`sensor_msgs/msg/PointCloud2`, XYZIRC format with intensity)
+- Camera image topics (`sensor_msgs/msg/Image`, BGR8 format)
+- Camera info topics (`sensor_msgs/msg/CameraInfo`, intrinsics and distortion coefficients)
+
+**Outputs**
+
+- `/diagnostics` (`diagnostic_msgs/msg/DiagnosticArray`): calibration status per camera-LiDAR pair
+- Preview image topics (`sensor_msgs/msg/Image`): visualization with projected points
+
+The node supports three runtime modes: `MANUAL` (on-demand validation via a `std_srvs/srv/Trigger`
+service), `PERIODIC` (validation at a configurable interval), and `ACTIVE` (continuous monitoring
+with synchronized sensor data). Inference is gated by configurable prerequisite checks on vehicle
+linear velocity, angular velocity, and the number of detected objects in the scene.
+
+## Usage in Autoware
+
+The node loads these artifacts from `~/autoware_data/ml_models/calibration_status_classifier/` and
+launches with:
+
+```bash
+ros2 launch autoware_calibration_status_classifier calibration_status_classifier.launch.xml
+```
+
+Add `build_only:=true` to build the TensorRT engine from the ONNX as a one-off pre-task, and
+`model_path:=...` to point at a non-default artifact directory. See the
+[package README](https://github.com/autowarefoundation/autoware_universe/tree/main/sensing/autoware_calibration_status_classifier)
+for the full parameter reference.
+
+## Training
+
+The model was trained with the AWML CalibrationStatusClassification project:
+<https://github.com/tier4/AWML/tree/main/projects/CalibrationStatusClassification>.
+It was trained on calibrated and miscalibrated LiDAR-camera data; the exact training datasets and
+schedules are not publicly documented beyond the AWML project page.
+
+## Provenance and versioning
+
+| Hugging Face tag | Original source |
+| --- | --- |
+| `v2.0` | `https://awf.ml.dev.web.auto/sensing/models/calibration_status_classifier/v2/` |
+
+This repository migrates the artifacts previously hosted at the URL above. Consumers should pin a
+tag (`--revision v2.0`) rather than tracking `main`.
+
+## Limitations
+
+- Input images must be BGR8 (8-bit per channel); input point clouds must contain intensity
+  (XYZIRC format).
+- The classifier judges calibration from the projected overlay, so scenes must satisfy the node's
+  prerequisite checks (motion and object-count gates) for reliable results.
+- Sensor setups that differ substantially from the training configuration may require retraining
+  via the AWML project.
+
+## References
+
+- AWML CalibrationStatusClassification: <https://github.com/tier4/AWML/tree/main/projects/CalibrationStatusClassification>
+- Consuming package: <https://github.com/autowarefoundation/autoware_universe/tree/main/sensing/autoware_calibration_status_classifier>

calibration_status_classifier.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13d6119e8dc73945ab0efe76b0c9492e2536187fdc7963ba62b8625900f54474
+size 44825085

deploy_metadata.yaml

@@ -0,0 +1 @@
+version: v2.0

ml_package_calibration_status_classifier.param.yaml

@@ -0,0 +1,7 @@
+/**:
+  ros__parameters:
+    max_depth: 128.0
+    dilation_size: 1
+    height: [1080, 1860, 2160]  # [min, opt, max]
+    width: [1920, 2880, 3840]  # [min, opt, max]
+