We utilize deep reinforcement learning to obtain favorable trajectories for visual-inertial system calibration.

Overview

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning

Update: The lastest code will be updated in this branch. Please switch to CORL2020 branch if you are looking for the Model-based Heuristic Deep RL approach.

Developed by Le Chen and Yunke Ao from Autonomous Systems Lab (ASL) at ETH Zurich.

1 Introduction

In this work we presents a novel formulation to learn a motion policy to be executed on a robot arm for automatic data collection for calibrating intrinsics and extrinsics jointly. Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration. Given the current pose and collected measurements, the learned policy generates the subsequent transformation that optimizes sensor calibration accuracy. The evaluations in simulation and on a real robotic system show that our learned policy generates favorable motion trajectories and collects enough measurements efficiently that yield the desired intrinsics and extrinsics with short path lengths. In simulation we are able to perform calibrations $10\times$ faster than hand-crafted policies, which transfers to a real-world speed up of $3\times$ over a human expert.

2 Usage

Our code is tested on Ubuntu 18.04 LTS (Bionic Beaver) and ROS Melodic Morenia with GPU GTX 1660 Ti and CUDA 11.2.

2.1 Build Instructions

  • Install required dependencies:
sudo apt-get install build-essential software-properties-common
sudo apt-get install bc curl ca-certificates fakeroot gnupg2 libssl-dev lsb-release libelf-dev bison flex
sudo apt-get install ros-melodic-moveit, ros-melodic-moveit-visual-tools, ros-melodic-cmake-modules
sudo apt-get install ros-melodic-libfranka ros-melodic-franka-ros, ros-melodic-joint-trajectory-controller
sudo apt-get install ros-melodic-vision-opencv ros-melodic-image-transport-plugins
sudo apt-get install python-setuptools python-rosinstall ipython libeigen3-dev libboost-all-dev doxygen
sudo apt-get install libopencv-dev libgtk-3-dev python-catkin-tools
sudo apt-get install python-matplotlib python-scipy python-git python-pip ipython
sudo apt-get install libtbb-dev libblas-dev liblapack-dev libv4l-dev, libpoco-dev

pip install opencv-python
pip install opencv-contrib-python
pip install --upgrade tensorflow
pip install python-igraph --upgrade
pip install pyyaml
pip install rospkg
pip install matplotlib
pip install pandas
pip install pytorch
pip install wandb
pip install PyKDL
pip install gym
  • Clone the repository and catkin build:
cd ~/catkin_ws
git clone https://github.com/clthegoat/Learn-to-Calibrate.git
cd Learn-to-Calibrate
git checkout master
cd ../
mv Learn-to-Calibrate src
catkin build
source ~/catkin_ws/devel/setup.bash

2.2 Configuration

  • Please change the file saving directory in franka_cal_sim_single/config/config.yaml before training or testing!

2.3 Running the code

2.3.1 Training:

  • In terminal 1:
source ~/catkin_ws/devel/setup.bash
roslaunch franka_cal_sim_single cam_imu_ext_che.launch
  • In terminal 2:
source ~/catkin_ws/devel/setup.bash
cd src/franka_cal_sim/python/algorithms
python RL_algo_sac_int_ext.py

2.3.2 Testing:

  • In terminal 1:
source ~/catkin_ws/devel/setup.bash
roslaunch franka_cal_sim_single cam_imu_ext_che.launch
  • In terminal 2:
source ~/catkin_ws/devel/setup.bash
cd src/franka_cal_sim/python/test_policies/
python RL_algo_sac_ext_int_test.py

3 Citing

Please cite the following paper when using our code for your research:

@article{chen2020learning,
  title={Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning},
  author={Chen, Le and Ao, Yunke and Tschopp, Florian and Cramariuc, Andrei and Breyer, Michel and Chung, Jen Jen and Siegwart, Roland and Cadena, Cesar},
  journal={arXiv preprint arXiv:2011.02574},
  year={2020}
}

4 Code reference:

Our code is based on the following repositories:

Owner
ETHZ ASL
ETHZ ASL
Domain Generalization with MixStyle, ICLR'21.

MixStyle This repo contains the code of our ICLR'21 paper, "Domain Generalization with MixStyle". The OpenReview link is https://openreview.net/forum?

Kaiyang 208 Dec 28, 2022
Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems

AequeVox Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems README under development. Python Packages Required

Sai Sathiesh 2 Aug 28, 2022
GAN JAX - A toy project to generate images from GANs with JAX

GAN JAX - A toy project to generate images from GANs with JAX This project aims to bring the power of JAX, a Python framework developped by Google and

Valentin Goldité 14 Nov 29, 2022
Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021)

Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021) Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma. We address the pr

Kranti Kumar Parida 33 Jun 27, 2022
Diverse Branch Block: Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception-like Unit (PyTorch) (CVPR-2021) DBB is a powerful ConvNet building block to replace regul

253 Dec 24, 2022
OpenAi's gym environment wrapper to vectorize them with Ray

Ray Vector Environment Wrapper You would like to use Ray to vectorize your environment but you don't want to use RLLib ? You came to the right place !

Pierre TASSEL 15 Nov 10, 2022
Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

shindo.py Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data stored in NumPy array Introduction Japa

RR_Inyo 3 Sep 23, 2022
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems - NAACL 2021

Fine-grained Post-training for Multi-turn Response Selection Implements the model described in the following paper Fine-grained Post-training for Impr

Janghoon Han 83 Dec 20, 2022
Codes and pretrained weights for winning submission of 2021 Brain Tumor Segmentation (BraTS) Challenge

Winning submission to the 2021 Brain Tumor Segmentation Challenge This repo contains the codes and pretrained weights for the winning submission to th

94 Dec 28, 2022
Convert onnx models to pytorch.

onnx2torch onnx2torch is an ONNX to PyTorch converter. Our converter: Is easy to use – Convert the ONNX model with the function call convert; Is easy

ENOT 264 Dec 30, 2022
Yolo ros - YOLO-ROS for HUAWEI ATLAS200

YOLO-ROS YOLO-ROS for NVIDIA YOLO-ROS for HUAWEI ATLAS200, please checkout for b

ChrisLiu 5 Oct 18, 2022
Bayesian Optimization Library for Medical Image Segmentation.

bayesmedaug: Bayesian Optimization Library for Medical Image Segmentation. bayesmedaug optimizes your data augmentation hyperparameters for medical im

Şafak Bilici 7 Feb 10, 2022
Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis

PyEmits, a python package for easy manipulation in time-series data. Time-series data is very common in real life. Engineering FSI industry (Financial

Descript 150 Dec 06, 2022
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed+Megatron trained the world's most powerful language model: MT-530B DeepSpeed is hiring, come join us! DeepSpeed is a deep learning optimizat

Microsoft 8.4k Dec 28, 2022
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

OpenPCDet OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. It is also the official code release o

OpenMMLab 3.2k Dec 31, 2022
[Pedestron] Generalizable Pedestrian Detection: The Elephant In The Room. @ CVPR2021

Pedestron Pedestron is a MMdetection based repository, that focuses on the advancement of research on pedestrian detection. We provide a list of detec

Irtiza Hasan 594 Jan 05, 2023
On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks

On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks We provide the code (in PyTorch) and datasets for our paper "On Size-Orient

Zemin Liu 4 Jun 18, 2022
BuildingNet: Learning to Label 3D Buildings

BuildingNet This is the implementation of the BuildingNet architecture described in this paper: Paper: BuildingNet: Learning to Label 3D Buildings Arx

16 Nov 07, 2022
This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).

NeurIPS 2021 (Spotlight): Task-Adaptive Neural Network Search with Meta-Contrastive Learning This is an official PyTorch implementation of Task-Adapti

Wonyong Jeong 15 Nov 21, 2022
The first public PyTorch implementation of Attentive Recurrent Comparators

arc-pytorch PyTorch implementation of Attentive Recurrent Comparators by Shyam et al. A blog explaining Attentive Recurrent Comparators Visualizing At

Sanyam Agarwal 150 Oct 14, 2022