feat: add lidar_apollo_instance_segmentation v1.0 artifacts (from awf.ml.dev.web.auto/perception/models/lidar_apollo_instance_segmentation)

.gitignore

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+# 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

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+---
+license: apache-2.0
+pipeline_tag: object-detection
+tags:
+  - autoware
+  - ros2
+  - autonomous-driving
+  - lidar
+  - point-cloud
+  - instance-segmentation
+  - apollo
+  - tensorrt
+  - onnx
+---
+
+# Apollo LiDAR Instance Segmentation for Autoware (`lidar_apollo_instance_segmentation`)
+
+CNN-based LiDAR instance segmentation models, used by the
+[`autoware_lidar_apollo_instance_segmentation`](https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_lidar_apollo_instance_segmentation)
+node in [Autoware](https://github.com/autowarefoundation/autoware).
+
+The models originate from the Baidu [Apollo](https://github.com/ApolloAuto/apollo) 3D obstacle perception
+stack (CNN segmentation). The original Apollo caffemodels were converted to ONNX for use in Autoware, and the
+node builds a TensorRT engine from the ONNX file on first launch. The node rasterizes the input point cloud
+into a bird's-eye-view feature map, runs the network, and clusters the per-cell predictions into labeled
+obstacle instances.
+
+## Model overview
+
+| | |
+| --- | --- |
+| Task | Instance segmentation of LiDAR point clouds into labeled obstacle clusters |
+| Architecture | Apollo CNN segmentation (CNNSeg) on a bird's-eye-view feature map, plus obstacle clustering in the node |
+| Detected classes | Apollo meta types mapped to Autoware labels: `CAR` (small vehicle), `BUS` (big vehicle), `MOTORCYCLE` (non-motorized vehicle), `PEDESTRIAN`; unmatched clusters are `UNKNOWN` |
+| Runtime | TensorRT (FP32 by default) via the `autoware_lidar_apollo_instance_segmentation` ROS 2 node |
+| Format | ONNX, converted from Apollo caffemodels (Autoware builds the TensorRT engine locally on first launch) |
+| License | Apache-2.0 (weights derived from Apollo, which is Apache-2.0 licensed) |
+
+## Variants in this repository
+
+One ONNX model per supported LiDAR sensor, selected via the `model` launch argument:
+
+| Variant | Launch value | Feature map grid | Range [m] | Intensity feature |
+| --- | --- | --- | --- | --- |
+| `vlp-16` (Velodyne VLP-16) | `model_16` | 672 x 672 | 70 | yes |
+| `hdl-64` (Velodyne HDL-64) | `model_64` | 672 x 672 | 70 | yes |
+| `vls-128` (Velodyne VLS-128) | `model_128` (default) | 864 x 864 | 90 | no |
+
+The per-variant parameter files (`vlp-16.param.yaml`, `hdl-64.param.yaml`, `vls-128.param.yaml`) live in the
+consuming package under `config/`, not in this repository. The values above come from those files. Supported
+LiDARs are the Velodyne 16, 64, and 128 beam sensors, but the package README notes that other LiDARs such as
+the Velodyne 32 can also be used with good accuracy.
+
+## Files
+
+| File | Description |
+| --- | --- |
+| `vlp-16.onnx` | CNN segmentation model for Velodyne VLP-16 |
+| `hdl-64.onnx` | CNN segmentation model for Velodyne HDL-64 |
+| `vls-128.onnx` | CNN segmentation model for Velodyne VLS-128 |
+| `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 files
+> on first launch (or via `build_only:=true`).
+
+## Inputs and outputs (as used by the node)
+
+**Input**: `input/pointcloud` (`sensor_msgs/msg/PointCloud2`), point cloud data from LiDAR sensors
+(default remap: `/sensing/lidar/pointcloud`).
+
+**Outputs**:
+
+- `output/labeled_clusters` (`tier4_perception_msgs/msg/DetectedObjectsWithFeature`): detected objects with
+  labeled point cloud clusters.
+- `debug/instance_pointcloud` (`sensor_msgs/msg/PointCloud2`): segmented point cloud for visualization.
+
+Pre-processing (bird's-eye-view feature map generation) and post-processing (2D clustering of the network
+output into obstacle instances, score thresholding via `score_threshold`) run in the node, not in the ONNX
+graphs.
+
+## Usage in Autoware
+
+The node expects these artifacts under `~/autoware_data/ml_models/lidar_apollo_instance_segmentation/` and
+launches with, e.g.:
+
+```bash
+ros2 launch autoware_lidar_apollo_instance_segmentation lidar_apollo_instance_segmentation.launch.xml \
+  model:=model_128 \
+  data_path:=$HOME/autoware_data/ml_models
+```
+
+`model:=model_16`, `model_64`, or `model_128` selects `vlp-16.onnx`, `hdl-64.onnx`, or `vls-128.onnx`
+respectively, together with the matching parameter file from the package's `config/` directory. Add
+`build_only:=true` to build the TensorRT engine from the ONNX as a one-off pre-task. See the
+[package README](https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_lidar_apollo_instance_segmentation)
+for the full parameter reference.
+
+## Training
+
+There is no training code for these models. The weights were trained by the Baidu Apollo project and released
+as caffemodels, which were later converted to ONNX for Autoware. Training datasets, schedules, and metrics are
+not publicly documented.
+
+Origin and related projects:
+
+- Apollo autonomous driving platform (original caffemodels and CNN segmentation design):
+  <https://github.com/ApolloAuto/apollo>
+- Initial Autoware integration of the Apollo CNN segmentation detector:
+  <https://github.com/k0suke-murakami/autoware_perception>
+- TensorRT wrapper library historically used by the node (current code uses `autoware_tensorrt_common`):
+  <https://github.com/lewes6369/tensorRTWrapper>
+
+The original caffemodel sources (pinned Apollo commits) are listed in the package README:
+
+- VLP-16: `modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel` (Apollo commit `88bfa5a`)
+- HDL-64: `modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel` (Apollo commit `88bfa5a`)
+- VLS-128: `modules/perception/production/data/perception/lidar/models/cnnseg/velodyne128/deploy.caffemodel` (Apollo commit `91844c8`)
+
+## Provenance
+
+| | |
+| --- | --- |
+| Original hosting | `https://awf.ml.dev.web.auto/perception/models/lidar_apollo_instance_segmentation/` (unversioned) |
+| This repository | `AutowareFoundation/lidar_apollo_instance_segmentation`, tag `v1.0` |
+
+The `v1.0` tag corresponds to the exact file set previously served from the unversioned
+`awf.ml.dev.web.auto` path.
+
+## Limitations
+
+- Each ONNX file is tuned for a specific Velodyne sensor (16, 64, or 128 beams); other LiDARs may work with
+  good accuracy but are not the intended configuration.
+- Only the classes listed above are produced; other road users are reported as `UNKNOWN`.
+- No training code or training data documentation exists, so the models cannot be retrained or fine-tuned from
+  public sources.
+
+## References
+
+- Apollo 3D Obstacle Perception description:
+  <https://github.com/ApolloAuto/apollo/blob/r7.0.0/docs/specs/3d_obstacle_perception.md>
+- Consuming Autoware package:
+  <https://github.com/autowarefoundation/autoware_universe/tree/main/perception/autoware_lidar_apollo_instance_segmentation>
+
+## Legal notice
+
+The model weights are derived from the Baidu Apollo project, which is licensed under the Apache License 2.0.
+The consuming Autoware package additionally incorporates code from the Apollo project (Apache-2.0), the
+tensorRTWrapper library (MIT), and the autoware_perception integration (Apache-2.0); see the package README
+for the full license texts.

deploy_metadata.yaml

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+version: v1.0

hdl-64.onnx

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+size 11906335

vlp-16.onnx

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vls-128.onnx

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