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PhysimGlobalPose

This repository implements a search-based technique for 6D pose estimation of objects in clutter as described in our paper pdf.

Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search.
By Chaitanya Mitash, Kostas Bekris, Abdeslam Boularias (Rutgers University).
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018.

Setup

Clone the repository.
Download object models: Models
Copy models to $PHYSIM_GLOBAL_POSE/src/physim_pose_estimation/
Download trained fcn weights: FCN Model
Copy weights to $PHYSIM_GLOBAL_POSE/src/3rdparty/fcn_segmentation_package
Download and extract Bullet

Demo

export BULLET_PHYSICS_PATH=/path/to/bullet/bullet3-2.86.1/
export PHYSIM_GLOBAL_POSE=/path/to/repo/PhysimGlobalPose
source $PHYSIM_GLOBAL_POSE/devel/setup.sh

cd $PHYSIM_GLOBAL_POSE/src
catkin_init_workspace
cd $PHYSIM_GLOBAL_POSE
catkin_make
rosrun physim_pose_estimation physim_pose_estimation
run $PHYSIM_GLOBAL_POSE/src/3rdparty/fcn_segmentation_package/predict
rosservice call /pose_estimation "APC" "$PHYSIM_GLOBAL_POSE/test-scene/" "FCNThreshold" "PCS" "LCP"

Output

Estimated 6D pose of all objects in the scene.

System Requirements

Ubuntu 14.04/16.04
Cuda 8.0, CudNN 5.0

Citing

To cite the work:

@inproceedings{mitash2017improving,
  Author = {Mitash, Chaitanya and Boularias, Abdeslam and Bekris, Kostas E},
  Booktitle = {{IEEE} International Conference on Robotics and Automation (ICRA)},
  Title = {Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search},
  Year = {2018}}

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