All Projects → 6-robot → wpb_home

6-robot / wpb_home

Licence: GPL-2.0 license
Source code for WPB ROS Robot

Programming Languages

C++
36643 projects - #6 most used programming language
CMake
9771 projects
python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to wpb home

awesome-ros-mobile-robot
😎 A curated list of awesome mobile robots study resources based on ROS (including SLAM, odometry and navigation, manipulation)
Stars: ✭ 284 (+456.86%)
Mutual labels:  manipulation, slam
slam gmapping
Slam Gmapping for ROS2
Stars: ✭ 56 (+9.8%)
Mutual labels:  slam
datawizard
Magic potions to clean and transform your data 🧙
Stars: ✭ 149 (+192.16%)
Mutual labels:  manipulation
Mei.js
a minimal, simple and helpful library for you
Stars: ✭ 15 (-70.59%)
Mutual labels:  manipulation
ba demo ceres
Bundle adjustment demo using Ceres Solver, with customized cost function and local parameterization on SE(3)
Stars: ✭ 150 (+194.12%)
Mutual labels:  slam
kPAM
kPAM: Generalizable Robotic Manipulation
Stars: ✭ 73 (+43.14%)
Mutual labels:  manipulation
SS-Replan
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
Stars: ✭ 43 (-15.69%)
Mutual labels:  manipulation
SRLCD
fast loop closure detection (online visual place recognition) via saliency re-identification IROS 2020
Stars: ✭ 78 (+52.94%)
Mutual labels:  slam
calvin
CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks
Stars: ✭ 105 (+105.88%)
Mutual labels:  manipulation
udacity-cvnd-projects
My solutions to the projects assigned for the Udacity Computer Vision Nanodegree
Stars: ✭ 36 (-29.41%)
Mutual labels:  slam
lt-mapper
A Modular Framework for LiDAR-based Lifelong Mapping
Stars: ✭ 301 (+490.2%)
Mutual labels:  slam
slam docker collection
A collection of docker environments for 3D SLAM packages
Stars: ✭ 130 (+154.9%)
Mutual labels:  slam
srrg2 slam interfaces
Plug-and-Play SLAM core repository. Atomic and compound SLAM modules
Stars: ✭ 76 (+49.02%)
Mutual labels:  slam
ProcessAdminActions
ProcessWire control panel for running various admin actions
Stars: ✭ 17 (-66.67%)
Mutual labels:  manipulation
puma
Poisson Surface Reconstruction for LiDAR Odometry and Mapping
Stars: ✭ 302 (+492.16%)
Mutual labels:  slam
JuliaAutonomy
Julia sample codes for Autonomy, Robotics and Self-Driving Algorithms.
Stars: ✭ 21 (-58.82%)
Mutual labels:  slam
ai for robotics
Visualizations of algorithms covered in Sebastian Thrun's excellent Artificial Intelligence for Robotics course on Udacity.
Stars: ✭ 125 (+145.1%)
Mutual labels:  slam
mast project
Underwater Camera and Sonar SLAM (Kevin and Linde's MAST class project)
Stars: ✭ 29 (-43.14%)
Mutual labels:  slam
pi roombot
ROS raspberry pi "roomba" like robot
Stars: ✭ 12 (-76.47%)
Mutual labels:  slam
tsdom
Fast, lightweight TypeScript DOM manipulation utility
Stars: ✭ 16 (-68.63%)
Mutual labels:  manipulation

启智ROS机器人开放源码

使用步骤

  1. 安装ROS.
    Kinetic/Ubuntu 16.04 安装步骤
  2. 配置好开发环境. 配置方法
  3. 安装依赖项:
    Kinetic/Ubuntu 16.04
cd ~/catkin_ws/src/wpb_home/wpb_home_bringup/scripts
./install_for_kinetic.sh

ROS Melodic/Ubuntu 18.04

cd ~/catkin_ws/src/wpb_home/wpb_home_bringup/scripts
./install_for_melodic.sh
  1. 获取源码:
cd ~/catkin_ws/src/
git clone https://github.com/6-robot/wpb_home.git
  1. 设置设备权限
roscd wpb_home_bringup
cd scripts
chmod +x create_udev_rules.sh
./create_udev_rules.sh 
  1. 编译
cd ~/catkin_ws
catkin_make
  1. 欢迎享用 :)

平台介绍

启智(ROS版)是北京六部工坊科技有限公司为ROS机器人算法开发量身打造的一款机器人硬件平台,拥有硬件里程计、激光测距雷达、立体视觉相机和语音输入和立体声输出等一整套部件,完美适配ROS的TF、Navigation、Actionlib和Pluginlib子系统,是深入学习ROS和高级机器人算法验证开发的理想平台。 wpb ros pic
wpb mani pic

硬件结构

spec pic

功能特性

1. URDF模型描述

启智ROS版具备完整的URDF模型描述,可以在ROS系统里直接加载。 1 pic

2. 电机码盘里程计

启智ROS版装备了带编码器的直流伺服电机,可以在ROS里接收电机码盘计数,从而推算出机器人的移动里程信息。 2 pic

3. IMU姿态传感

启智ROS版内置了一枚六轴的IMU单元,可以实时获取机器人的滚转、倾斜和朝向信息,为机器人的上层控制算法提供数值依据。 3 pic

4. 三维立体视觉

启智ROS版采用最新一代的TOF立体相机,探测距离达到8米,最大视角70°,适用于对室内环境的三维模型重构。 4 pic

5. SLAM环境建图

启智ROS版装备了新一代的360°激光雷达,可以实时扫描机器人周围的障碍物分布状况,借助HectorSLAM和GMapping算法,创建环境地图。 5 pic

6. 自主定位导航

启智ROS版将激光雷达扫描的距离信息与电机里程计数据进行融合,使用AMCL方法进行地图定位,结合ROS的move_base进行自主导航。 6 pic

7. 动态目标跟随

启智ROS版可以快速锁定一个跟踪目标,保持指定距离,一直尾随目标物进行移动。 7 pic

8. 物品检测

启智ROS版通过立体相机获得三维点云,对点云中的物品进行检测、匹配和轮廓辨识,计算每个物品的外形尺寸和三维空间坐标。 8 pic

9. 物品抓取

启智ROS版可扩展安装模块化机械臂,在物品检测的基础上实现物品抓取功能。 9 pic

10. 人脸检测

启智ROS版支持Haar特征级联分类器,结合机器人头部的高分辨率摄像机,对环境中的人脸特征进行检测,并根据立体相机采集的点云,计算其三维空间坐标 10 pic

11. 传感器融合

启智ROS版可以将立体相机三维点云和激光雷达SLAM二维地图进行融合,更好整合环境信息。 11 pic

12. 语音识别

启智ROS版使用卡耐基梅隆大学开发的PocketSphinx语音识别引擎,可以对语音指令进行识别。 12 pic

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].