The camera_pose_initializer node estimates the vehicle's initial pose from a camera image at the request of AD API. It segments the road surface in the undistorted camera image with this model and matches the result against the Lanelet2 vector map to score initial pose candidates.

The model is Intel Open Model Zoo's road-segmentation-adas-0001, converted to a TensorFlow frozen graph and to ONNX via the PINTO model zoo (entry 136). The node loads the frozen graph on CPU through OpenCV DNN; the ONNX export is shipped alongside it as the migration path off OpenCV's legacy TensorFlow importer (see Why ONNX is included).

Repository versions

Tag	Contents	Purpose
`v0.1`	The complete upstream PINTO model zoo export, all 104 files	Archival snapshot of exactly what Autoware's previous hosting served, kept so the provenance chain survives the decommissioning of the old S3 bucket
`v1.0`	`saved_model/model_float32.pb` and `saved_model/model_float32.onnx`	What Autoware installs. `main` tracks this tag

Consumers must pin a revision explicitly, never main. Autoware's ansible artifacts role pins v0.1 while the artifact hosting migration lands, so that move delivers exactly the same files as the old hosting did, and switches to v1.0 in a separate follow-up (see autowarefoundation/autoware#7223).

v0.1 additionally contains the TFLite variants (float32, float16, dynamic-range and full integer quantized), the TensorFlow SavedModel with its checkpoint, the TensorFlow.js graph models, the Intel OpenVINO IR pairs (FP32 and FP16) and the Myriad blob, and two TF-TRT converted SavedModels with their prebuilt TensorRT engines. None of them is loaded by Autoware. The TF-TRT engines in particular are 2021-vintage TensorRT 8.0 plans that cannot deserialize on a current stack: Autoware builds TensorRT engines locally on first launch and never ships them. They are archived in v0.1 for completeness only.

Model overview


Task	Road semantic segmentation of a camera image, used for camera-based initial pose estimation
Origin	Intel Open Model Zoo `road-segmentation-adas-0001`, converted by the PINTO model zoo
Runtime	OpenCV DNN (`cv::dnn::readNet`), OpenCV backend, CPU target
Formats	TensorFlow frozen graph float32 (`model_float32.pb`), ONNX opset 11 (`model_float32.onnx`)
Network input	512 x 896 RGB image, float32, scale 1.0, no mean subtraction. The frozen graph presents it as a `1 x 3 x 512 x 896` NCHW blob through OpenCV; the ONNX graph boundary is NHWC, `1 x 512 x 896 x 3`
Network output	4-channel softmax score map (background, road, curb, marking), NCHW for the frozen graph and NHWC for the ONNX
License	Apache-2.0 (Intel Open Model Zoo)

For the class definitions and architecture details of the segmentation network, see the upstream Intel Open Model Zoo model page.

Files

Contents of v1.0:

File	Description
`saved_model/model_float32.pb`	TensorFlow frozen graph, float32; the file the node loads today
`saved_model/model_float32.onnx`	The same network exported to ONNX opset 11 by tf2onnx; not loaded today, see below
`deploy_metadata.yaml`	Deployment metadata recording the artifact version of this repository

The directory layout (saved_model/model_float32.pb) is preserved exactly as the package's launch file expects it.

Why ONNX is included

The frozen graph and the ONNX file are the same network with the same weights, but they do not behave the same on current OpenCV releases.

Running the node's own call sequence (cv::dnn::readNet, OpenCV backend, CPU target, blobFromImage at 896 x 512, forward) against model_float32.pb:

OpenCV	Softmax normalized over	Result
4.6.0, 4.8.1	channel axis	correct, the 4 class scores sum to 1.0 per pixel
4.9.0, 4.10.0	width axis	wrong, class scores sum to 0.0059 and the output mean is exactly 1/896

On 4.9 and newer, OpenCV's TensorFlow importer applies the terminal softmax along the wrong axis. The output shape and the layer name are unchanged and no error is raised, so the failure is silent: at the node's default score threshold of 0.5 no pixel is ever selected. model_float32.onnx produces correct output on all four versions.

Autoware currently builds against OpenCV 4.5.4 (Ubuntu 22.04) and 4.6.0 (Ubuntu 24.04), which are unaffected. Ubuntu 26.04 LTS ships OpenCV 4.10.0. The ONNX file is therefore distributed so the consuming node can move to cv::dnn::readNetFromONNX, ONNX Runtime or TensorRT without needing the deprecated hosting or a reconversion. Note that the ONNX graph is NHWC at its boundary, so the node's blob construction and output conversion need to be adapted when it switches.

Inputs and outputs (as used by the node)

Subscriptions

Topic	Type	Description
`~/input/camera_info`	`sensor_msgs/msg/CameraInfo`	undistorted camera info
`~/input/image_raw`	`sensor_msgs/msg/Image`	undistorted camera image
`~/input/vector_map`	`autoware_map_msgs/msg/LaneletMapBin`	vector map

Publications

Topic	Type	Description
`~/debug/init_candidates`	`visualization_msgs/msg/MarkerArray`	initial pose candidates (the package README lists this topic as `output/candidates`, but the node publishes it under `debug/init_candidates`)

Services

Service	Type	Description
`~/yabloc_align_srv`	`autoware_internal_localization_msgs/srv/PoseWithCovarianceStamped`	initial pose estimation request

Pre-processing and post-processing run in the node: the image is resized to 896 x 512 and converted to a float32 RGB blob; the 4-channel output score map is resized back to the image resolution, the first (background) channel is dropped, and the remaining three channels are thresholded into a binary mask image used for map matching.

The node's only ROS parameter besides model_path is angle_resolution (default 30, the number of divisions of the 1 sigma angle range).

Usage in Autoware

Autoware downloads this artifact to ~/autoware_data/ml_models/yabloc_pose_initializer/ during environment setup (the ansible artifacts role). To fetch it manually:

bash

hf download AutowareFoundation/yabloc_pose_initializer --revision v1.0 \
  --local-dir ~/autoware_data/ml_models/yabloc_pose_initializer

The package's launch file resolves the model at:

$HOME/autoware_data/ml_models/yabloc_pose_initializer/saved_model/model_float32.pb

and passes it to the node as the model_path parameter (overridable via the model_path launch argument). The node is started as part of the YabLoc localization stack; see the package README for details. If the model is missing, initialization still completes, but accuracy may be compromised.

Training

This model was not trained by the Autoware project; it is redistributed as-is from upstream:

Original model: Intel Open Model Zoo road-segmentation-adas-0001 (https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/road-segmentation-adas-0001), licensed under Apache License 2.0.
Conversion to TensorFlow and ONNX: PINTO model zoo entry 136 (https://github.com/PINTO0309/PINTO_model_zoo/tree/main/136_road-segmentation-adas-0001); the conversion scripts are released under the MIT license.

Training datasets, schedules, and metrics are not documented in the Autoware sources; refer to the Intel Open Model Zoo model page for upstream details.

Provenance and versioning


Original source	`https://autoware-files.s3.us-west-2.amazonaws.com/models/yabloc/136_road-segmentation-adas-0001/resources.tar.gz` (unversioned tarball)
Upstream mirror	`https://s3.ap-northeast-2.wasabisys.com/pinto-model-zoo/136_road-segmentation-adas-0001/resources.tar.gz` (PINTO model zoo entry 136)
Source `resources.tar.gz` sha256	`1f660e15f95074bade32b1f80dbf618e9cee1f0b9f76d3f4671cb9be7f56eb3a` (the archive itself is not redistributed here; `v0.1` holds its extracted contents)
`saved_model/model_float32.pb` sha256	`c4e373552f4efb91592ed99f49afc6df179f8a95174ceaddd1ab35692f435b47`
`saved_model/model_float32.onnx` sha256	`931f80db4faa9eb37fafaaec35cdd6c7498a0b8afb45e335a535da028d27e50a`

Every file in v0.1 is byte-identical to the corresponding member of that tarball.

Limitations

Inference runs on CPU via OpenCV DNN; the node does not use a GPU for this model.
The network operates at a fixed 896 x 512 input resolution; images are resized by the node.
The model was trained upstream by Intel, not on Autoware-specific data; segmentation quality on cameras or scenes that differ from the upstream training domain is not characterized here.
Pose initialization quality depends on the vector map and the undistorted camera input; a missing or poorly matching segmentation degrades the initial pose accuracy.
The frozen graph is affected by the OpenCV importer regression described above on OpenCV 4.9 and newer.

References

Intel Open Model Zoo, road-segmentation-adas-0001: https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/road-segmentation-adas-0001
PINTO model zoo, entry 136: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/136_road-segmentation-adas-0001
Consuming package: https://github.com/autowarefoundation/autoware_universe/tree/main/localization/yabloc/yabloc_pose_initializer
Autoware: https://github.com/autowarefoundation/autoware

Acknowledgment

Special thanks to openvinotoolkit/open_model_zoo and PINTO0309 for providing and converting the original model.

Legal Notice

The original model is distributed by Intel Open Model Zoo under the Apache License, Version 2.0. The PINTO model zoo conversion scripts are released under the MIT license. See the upstream repositories for the full license terms.

AutowareFoundation/yabloc_pose_initializer

Road Segmentation for Autoware YabLoc (yabloc_pose_initializer)