Mono-RPF

Robot Peron Following Under Partial Occlusion

Install

Prequities

• ROS, verified in melodic and noetic
• OpenCV with 3.4.12
• Ceres

1. Create a conda environment and install pytorch

conda create -n mono_following python=3.8
conda activate mono_following
# This is based on your GPU settings, other settings should be careful
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

2. Install python related packages:

pip install -r requirements.txt
git clone https://github.com/eric-wieser/ros_numpy
cd ros_numpy
python setup.py install

3. Install cpp related packages:

• OpenCV==3.4.12
• Eigen==3.0+

Download pre-trained weights

1. Download bounding-box detection models from Google Drive or [YOLOX_deepsort_tracker], then make director mono_tracking/scripts/AlphaPose/YOLOX/weights and put the checkpoints to it.
2. Download 2d joint detection models: Google drive, then make directory mono_tracking/scripts/AlphaPose/Models and put the checkpoints to it.

How to use

Run with our self-built dataset as ROSBAG:

roslaunch mono_tracking all_mono_tracking.launch sim:=true
# play bag
rosbag play --clock -r 0.2 2022-07-15-17-09-34.bag

Run with our DINGO:

roslaunch mono_tracking all_mono_tracking.launch sim:=false

Run with icvs datasets as ROSBAG, and evaluate:

# If run in "corridor_corners" scene
roslaunch mono_tracking evaluate_MPF_in_icvs.launch scene:=corridor_corners

Citation

@inproceedings{ye2023robot,
  title={Robot Person Following Under Partial Occlusion},
  author={Ye, Hanjing and Zhao, Jieting and Pan, Yaling and Chen, Weinan and He, Li and Zhang, Hong},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={7591--7597},
  year={2023},
  organization={IEEE}
}

Acknowledgement

• monocular_person_following
• YOLOX_deepsort_tracker
• AlphaPose