Foundation Pose Node

FoundationPose

Derived from FoundationPose

@InProceedings{foundationposewen2024,
author        = {Bowen Wen, Wei Yang, Jan Kautz, Stan Birchfield},
title         = {{FoundationPose}: Unified 6D Pose Estimation and Tracking of Novel Objects},
booktitle     = {CVPR},
year          = {2024},
}

@InProceedings{bundlesdfwen2023,
author        = {Bowen Wen and Jonathan Tremblay and Valts Blukis and Stephen Tyree and Thomas M\"{u}ller and Alex Evans and Dieter Fox and Jan Kautz and Stan Birchfield},
title         = {{BundleSDF}: {N}eural 6-{DoF} Tracking and {3D} Reconstruction of Unknown Objects},
booktitle     = {CVPR},
year          = {2023},
}

Using models of Ultralytics such as

@software{yolo26_ultralytics,
  author = {Glenn Jocher and Jing Qiu},
  title = {Ultralytics YOLO26},
  version = {26.0.0},
  year = {2026},
  url = {https://github.com/ultralytics/ultralytics},
  orcid = {0000-0001-5950-6979, 0000-0003-3783-7069},
  license = {AGPL-3.0}
}

Summary of changes

1. Detection and segmentation This repository integrates FoundationPose with a detection and segmentation model (from ultralytics for now)

2. ROS Node The pipeline is turned into a ROS2 Node with the following input and output :

• Input : RGB (CompressedImage), Depth (CompressedImage), camera intrinsics (CameraInfo), object mesh model (.obj file)
• Output : 6D Pose (PoseStamped)

3. Simplified Dockerfile It includes a simplified Dockerfile compared to the original version (does not using conda in the docker). The installation contains FoundationPose dependencies and ROS dependencies.

Install

docker build --network host -f docker/dockerfile -t foundationposev2 .

Run

bash ./docker/run_container.sh 
python node.py

Parameters

• mesh_file
• target_object
• est_refine_iter
• track_refine_iter
• debug
• debug_dir
• depth_scale
• color_topic
• depth_topic
• camera_info_topic
• pose_frame_id
• slop
• seg_model_name
• resize_factor
• min_initial_detection_counter
• enable_pose_tracking