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
Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

BEVNet Datasets Datasets should be put inside data/. For example, data/semantic_kitti_4class_100x100. Training BEVNet-S Example: cd experiments bash t

(Brian) JoonHo Lee 24 Dec 12, 2022
CVPR2021: Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Temporal Context Aggregation Network - Pytorch This repo holds the pytorch-version codes of paper: "Temporal Context Aggregation Network for Temporal

Zhiwu Qing 63 Sep 27, 2022
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

Stanford Intelligent Systems Laboratory 9 Jun 06, 2022
[ICLR 2021] HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark

HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark Accepted as a spotlight paper at ICLR 2021. Table of content File structure Prerequi

72 Jan 03, 2023
Episodic-memory - Ego4D Episodic Memory Benchmark

Ego4D Episodic Memory Benchmark EGO4D is the world's largest egocentric (first p

3 Feb 18, 2022
A novel framework to automatically learn high-quality scanning of non-planar, complex anisotropic appearance.

appearance-scanner About This repository is an implementation of the neural network proposed in Free-form Scanning of Non-planar Appearance with Neura

Xiaohe Ma 14 Oct 18, 2022
Code and project page for ICCV 2021 paper "DisUnknown: Distilling Unknown Factors for Disentanglement Learning"

DisUnknown: Distilling Unknown Factors for Disentanglement Learning See introduction on our project page Requirements PyTorch = 1.8.0 torch.linalg.ei

Sitao Xiang 24 May 16, 2022
Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

Microsoft 11.3k Dec 30, 2022
Overview of architecture and implementation of TEDS-Net, as described in MICCAI 2021: "TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee TopologyPreservation in Segmentations"

TEDS-Net Overview of architecture and implementation of TEDS-Net, as described in MICCAI 2021: "TEDS-Net: Enforcing Diffeomorphisms in Spatial Transfo

Madeleine K Wyburd 14 Jan 04, 2023
PyTorch implementation of UNet++ (Nested U-Net).

PyTorch implementation of UNet++ (Nested U-Net) This repository contains code for a image segmentation model based on UNet++: A Nested U-Net Architect

4ui_iurz1 642 Jan 04, 2023
Async API for controlling Hue Lights

Hue API Async API for controlling Hue Lights Documentation: hue-api.nirantak.com Source: github.com/nirantak/hue-api Installation This is an async cli

Nirantak Raghav 4 Nov 16, 2022
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

nli2paraphrases Source code repository accompanying the preprint Extracting and filtering paraphrases by bridging natural language inference and parap

Matej Klemen 1 Mar 09, 2022
PyTorch implementation of neural style transfer algorithm

neural-style-pt This is a PyTorch implementation of the paper A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias

770 Jan 02, 2023
Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

Bootstrap Your Own Latent (BYOL), in Pytorch Practical implementation of an astoundingly simple method for self-supervised learning that achieves a ne

Phil Wang 1.4k Dec 29, 2022
Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques This repository is derived from the NMTGMinor

Tu Anh Dinh 1 Sep 07, 2022
Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows

Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows This is the official implementation of the ICCV 2021 Paper "Probabilistic Mono

62 Nov 23, 2022
Bayesian algorithm execution (BAX)

Bayesian Algorithm Execution (BAX) Code for the paper: Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mut

Willie Neiswanger 38 Dec 08, 2022
A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK

Pytorch-MBNet A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK Training To train a new model, please ru

46 Dec 28, 2022
Code for the paper "Reinforcement Learning as One Big Sequence Modeling Problem"

Trajectory Transformer Code release for Reinforcement Learning as One Big Sequence Modeling Problem. Installation All python dependencies are in envir

Michael Janner 269 Jan 05, 2023
Realtime micro-expression recognition using OpenCV and PyTorch

Micro-expression Recognition Realtime micro-expression recognition from scratch using OpenCV and PyTorch Try it out with a webcam or video using the e

Irfan 35 Dec 05, 2022