Coder Social home page Coder Social logo

standardgalactic / motioncapturejointcalibration.jl Goto Github PK

View Code? Open in Web Editor NEW

This project forked from juliarobotics/motioncapturejointcalibration.jl

0.0 2.0 0.0 109 KB

Kinematic calibration for robots using motion capture data

License: Other

Julia 88.80% Jupyter Notebook 11.20%

motioncapturejointcalibration.jl's Introduction

MotionCaptureJointCalibration

Build Status codecov.io

MotionCaptureJointCalibration provides functionality for kinematic calibration of robots, given measurements of the positions of motion capture markers attached to the robot's links and positions of the robot's joints in a number of poses. It does so by solving a nonlinear program (NLP) with (weighted) square error between measured and predicted marker locations as the objective to minimize.

MotionCaptureJointCalibration is a small Julia library built on top of JuMP and RigidBodyDynamics.jl. JuMP makes it possible to choose between various NLP solvers. Ipopt appears to perform fairly well for the problems formulated by this package.

News

  • October 18, 2017: tagged version 0.0.1.
  • August 4, 2017: the package is under initial construction.

Features

Features include:

  • handling of occlusions
  • handling of measurements of the body-fixed locations of only a subset of the markers attached to the robot (the unknown marker positions will be solved for, given rough bounds)
  • handling of measurements of only a subset of a robot's joint positions (the unknown joint positions will be solved for, given rough bounds)
  • proper handling of quaternion-parameterized floating joints (unit norm constraints for quaternions)
  • visualization of calibration results using RigidBodyTreeInspector

Currently, MotionCaptureJointCalibration can only estimate constant offsets between measured and actual joint positions.

Installation

To install, simply run

Pkg.add("MotionCaptureJointCalibration")

This will install MotionCaptureJointCalibration and its required dependencies. RigidBodyTreeInspector.jl is an optional dependency and can be used to visualize the calibration results (Pkg.add("RigidBodyTreeInspector")). You'll also need an NLP solver that interfaces with JuMP, e.g. Ipopt (Pkg.add("Ipopt")).

Usage

See the demo notebook for usage.

Acknowledgements

A variant of the NLP formulation used in this package is due to Michael Posa.

motioncapturejointcalibration.jl's People

Contributors

femtocleaner[bot] avatar github-actions[bot] avatar juliatagbot avatar tkoolen avatar traversaro avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.