Displaying 1 to 20 from 354 results

P - The P programming language.

  •    CSharp

P is a language for asynchronous event-driven programming. P allows the programmer to specify the system as a collection of interacting state machines, which communicate with each other using events. P unifies modeling and programming into one activity for the programmer. Not only can a P program be compiled into executable code, but it can also be validated using systematic testing. P has been used to implement and validate the USB device driver stack that ships with Microsoft Windows 8 and Windows Phone. P is also suitable for the design and implementation of networked, embedded, and distributed systems.Check the P manual.

Johnny-Five - JavaScript Robotics and IoT programming framework

  •    Javascript

Johnny-Five is an Open Source, Firmata Protocol based, IoT and Robotics programming framework, developed at Bocoup. Johnny-Five programs can be written for Arduino (all models), Electric Imp, Beagle Bone, Intel Galileo & Edison, Linino One, Pinoccio, pcDuino3, Raspberry Pi, Particle/Spark Core & Photon, Tessel 2, TI Launchpad and more!

opencog - A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)

  •    Scheme

OpenCog is a framework for developing AI systems, especially appropriate for integrative multi-algorithm systems, and artificial general intelligence systems. Though much work remains to be done, it currently contains a functional core framework, and a number of cognitive agents at varying levels of completion, some already displaying interesting and useful functionalities alone and in combination. With the exception of MOSES and the CogServer, all of the above are in active development, are half-baked, poorly documented, mis-designed, subject to experimentation, and generally in need of love an attention. This is where experimentation and integration are taking place, and, like any laboratory, things are a bit fluid and chaotic.

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.




Open3D - Open3D: A Modern Library for 3D Data Processing

  •    C++

Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. We welcome contributions from the open-source community. Please cite our work if you use Open3D.


simbody - High-performance C++ multibody dynamics/physics library for simulating articulated biomechanical and mechanical systems like vehicles, robots, and the human skeleton

  •    C++

Simbody is a high-performance, open-source toolkit for science- and engineering-quality simulation of articulated mechanisms, including biomechanical structures such as human and animal skeletons, mechanical systems like robots, vehicles, and machines, and anything else that can be described as a set of rigid bodies interconnected by joints, influenced by forces and motions, and restricted by constraints. Simbody includes a multibody dynamics library for modeling motion in generalized/internal coordinates in O(n) time. This is sometimes called a Featherstone-style physics engine. Read more about Simbody at the Simbody homepage.

redtail - Perception and AI components for autonomous mobile robotics.

  •    C++

Autonomous visual navigation components for drones and ground vehicles using deep learning. Refer to wiki for more information on how to get started. This project contains deep neural networks, computer vision and control code, hardware instructions and other artifacts that allow users to build a drone or a ground vehicle which can autonomously navigate through highly unstructured environments like forest trails, sidewalks, etc. Our TrailNet DNN for visual navigation is running on NVIDIA's Jetson embedded platform. Our arXiv paper describes TrailNet and other runtime modules in detail.

ros_best_practices - Best practices, conventions, and tricks for ROS

  •    C++

Best practices for ROS2 in the making. See the Foxy branch in the meanwhile. This is a loose collection of best practices, conventions, and tricks for using the Robot Operating System (ROS). It builds up on the official ROS documentation and other resources and is meant as summary and overview.

evo - Python package for the evaluation of odometry and SLAM

  •    Python

This package provides executables and a small library for handling, evaluating and comparing the trajectory output of odometry and SLAM algorithms. See here for more infos about the formats.

ompl - The Open Motion Planning Library (OMPL)

  •    C++

Visit the OMPL installation page for detailed installation instructions.

Kimera - Index repo for Kimera code

  •    

Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. Kimera is partially funded by ARL DCIST, ONR RAIDER, MIT Lincoln Laboratory, and “la Caixa” Foundation (ID 100010434), LCF/BQ/AA18/11680088 (A. Rosinol).

Kimera-VIO - Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation.

  •    C++

For evaluation plots, check our jenkins server. Kimera-VIO is a Visual Inertial Odometry pipeline for accurate State Estimation from Stereo + IMU data. It can optionally use Mono + IMU data instead of stereo cameras.

TEASER-plusplus - A fast and robust point cloud registration library

  •    C++

TEASER++ is a fast and certifiably-robust point cloud registration library written in C++, with Python and MATLAB bindings. Left: correspondences generated by 3DSmoothNet (green and red lines represent the inlier and outlier correspondences according to the ground truth respectively). Right: alignment estimated by TEASER++ (green dots represent inliers found by TEASER++).

AB3DMOT - (IROS 2020, ECCVW 2020) Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics"

  •    Python

3D multi-object tracking (MOT) is an essential component technology for many real-time applications such as autonomous driving or assistive robotics. However, recent works for 3D MOT tend to focus more on developing accurate systems giving less regard to computational cost and system complexity. In contrast, this work proposes a simple yet accurate real-time baseline 3D MOT system. We use an off-the-shelf 3D object detector to obtain oriented 3D bounding boxes from the LiDAR point cloud. Then, a combination of 3D Kalman filter and Hungarian algorithm is used for state estimation and data association. Although our baseline system is a straightforward combination of standard methods, we obtain the state-of-the-art results. To evaluate our baseline system, we propose a new 3D MOT extension to the official KITTI 2D MOT evaluation along with two new metrics. Our proposed baseline method for 3D MOT establishes new state-of-the-art performance on 3D MOT for KITTI, improving the 3D MOTA from 72.23 of prior art to 76.47. Surprisingly, by projecting our 3D tracking results to the 2D image plane and compare against published 2D MOT methods, our system places 2nd on the official KITTI leaderboard. Also, our proposed 3D MOT method runs at a rate of 214.7 FPS, 65 times faster than the state-of-the-art 2D MOT system. 1. Clone the github repository.

rtabmap - RTAB-Map library and standalone application

  •    C++

RTAB-Map library and standalone application. For more information, visit the RTAB-Map's home page or the RTAB-Map's wiki.






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