Giter Site home page Giter Site logo

accelnet_challenge_sdu's Introduction

2021-2022 AccelNet Surgical Robotics Challenge, SDU

This is the submission to the 2021-2022 AccelNet Surgical Robotics Challenge from SDU Robotics University of Southern Denmark, Odense. I am submitting a solution to challenge #1 only. Although we originally planned to also submit solutions to tasks #2 and #3 I have not had the resources to finish them.

Our needle pose estimation pipeline uses a U-net-like CNN for pixel segmentation and runs very slow on a CPU. With a GeForce RTX 3070 Ti I see segmentation rates of 4-5 FPS on 1920x1080 images. Note that the first image in the pipeline is processed significantly slower than the subsequent ones.

We use the suture thread for determining the needle's orientation and request that you run the simulated environment that includes the thread.

Run procedure:

Our submission is set up as a catkin package. It can be run using the following procedure:

  • Put the accelnet_challenge_sdu package in a catkin workspace's src dir.
  • Build the workspace with catkin build or possibly catkin_make.
  • Source the devel/setup.bash.
  • Run the AMBF simulator ambf_simulator ambf_simulator --launch_file surgical_robotics_challenge/launch.yaml -l 0,1,3,4,14,15 -p 120 -t 1 --override_max_comm_freq 120
  • Run CRTK interface python3 surgical_robotics_challenge/scripts/surgical_robotics_challenge/launch_crtk_interface.py
  • Run evaluation script python3 evaluation.py -t sdu -e 1
  • Run our solution to task #1 with roslaunch accelnet_challange_sdu task1.roslaunch.

Dependencies:

We use the following non-standard Python packages (installable via pip)

  • open3d (tested with 0.15.2)
  • tensorflow (tested with 2.8.0 and 2.9.1)
  • scipy (tested with 1.8.1)
  • numpy-quaternion (tested with 2022.4.2)

Apart from these we use packages that should be installed with ROS.

Testing hardware

We've tested our work a desktop with Ryzen 5 5600X, GeForce RTX 3070 Ti (with CUDA), 32 GB RAM and on a laptop with Intel i7-1165G7, Integrated Graphics, 32 GB RAM. Both machines were running Ubuntu 20.04 with ROS Noetic.

Contact

Kim Lindberg Schwaner [email protected]

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.