Displaying 1 to 11 from 11 results

tf-pose-estimation - Deep Pose Estimation implemented using Tensorflow with Custom Architectures for fast inference

  •    PureBasic

'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices. 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess 2018.2.7 Arguments in run.py script changed. Support dynamic input size.

depth_clustering - :taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor

  •    C++

This is a fast and robust algorithm to segment point clouds taken with Velodyne sensor into objects. It works with all available Velodyne sensors, i.e. 16, 32 and 64 beam ones. I recommend using a virtual environment in your catkin workspace (<catkin_ws> in this readme) and will assume that you have it set up throughout this readme. Please update your commands accordingly if needed. I will be using pipenv that you can install with pip.

ifopt - An Eigen-based, light-weight C++ Interface to Nonlinear Programming Solvers (Ipopt, Snopt)

  •    C++

A modern, light-weight, Eigen-based C++ interface to Nonlinear Programming solvers, such as Ipopt and Snopt. An optimization problem consists of multiple independent variable- and constraint-sets. Each set represents a common concept, e.g. a set of variables might represents spline coefficients, another footstep positions. Similarly, a constraint-set groups similar constraints together. ifopt allows users to define each of these sets independently in separate classes and then builds the overall problem from these sets. (No more worrying adapting indices when adding or removing sets).

catkin_lint - Check catkin packages for common errors

  •    Python

catkin_lint checks package configurations for the catkin build system of ROS. You can find the documentation with installation instructions at http://fkie.github.io/catkin_lint.

ros-video-recorder - Image Record Package for ROS

  •    Python

Image Record Package for ROS. This node use opencv to save video, subscribe multiple image-publishing nodes.

industrial_ci - Easy continuous integration repository for ROS repositories

  •    Shell

CI (Continuous Integration) configuration for ROS (Robot Operating System). Other than the brief introduction in this page, you can also check the detailed doc here.

ros_qtc_plugin - ROS Qt Creator Plug-in (https://ros-qtc-plugin.readthedocs.io)

  •    C++

This repository contains simple to install and use ROS-related plugins for the Qt Creator (4.4) IDE. See the repository wiki for additional information. The ROS Qt Creator Plug-in is developed specifically for ROS to increase a developers' efficiency by simplifying tasks and creating a centralized location for ROS tools. Since it is built on top of the Qt Creator platform, users have access to all of its existing features like: syntax highlighting, code indexing, editors (C++ , Python, etc.), code completion, version control (Git, Subversion, etc.), debuggers (GDB, CDB, LLDB, etc.), and much more.

tensorflow_ros_cpp - Catkin-friendly C++ bindings for tensorflow.

  •    CMake

A Catkin-friendly package for utilizing the C++ API of Tensorflow. See the usage example at [https://github.com/tradr-project/tensorflow_ros_test].

catkin_tools_fetch - :dog2: "fetch" and "update" dependencies of projects in your catkin workspace with a new verb "dependencies" for catkin_tools

  •    Python

Defines new verb dependencies (or in short deps) and its sub-verbs fetch and update for catkin_tools. This verb is responsible for downloading external dependencies of the projects in a catkin workspace and keeping them up to date. For now only git is supported. The tool is under heavy development. Please use PyPI or download a tag for a stable version. Both available subverbs should be used after dependencies verb (or its shorter version deps) from within of a catkin workspace. Below are short showcases on usage of the verbs. Optional arguments are shown in brackets.

la3dm - Learning-aided 3D mapping

  •    C++

A suite of algorithms for learning-aided mapping. Includes implementations of Gaussian process regression and Bayesian generalized kernel inference for occupancy prediction using test-data octrees. This framework also contains the components necessary to run OctoMap as a baseline. This implementation as it stands now is primarily intended to enable replication of these methods over a few datasets. In addition to the implementation of relevant learning algorithms and data structures, we provide two sets of range data (sim_structured and sim_unstructured) collected in Gazebo for demonstration. Parameters of the sensors and environments are set in the relevant yaml files contained in the config/datasets directory, while configuration of parameters for the mapping methods can be found in config/methods.

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