# ROS Wrapper for Intel® RealSense™ Devices
These are packages for using Intel RealSense cameras (D400 series SR300 camera and T265 Tracking Module) with ROS.
## Installation Instructions
The following instructions support ROS Indigo, on **Ubuntu 14.04**, and ROS Kinetic, on **Ubuntu 16.04**.
#### The simplest way to install on a clean machine is to follow the instructions on the [.travis.yml](https://github.com/intel-ros/realsense/blob/development/.travis.yml) file. It basically summerize the elaborate instructions in the following 3 steps:
### Step 1: Install the latest Intel® RealSense™ SDK 2.0
- #### Install from [Debian Package](https://github.com/IntelRealSense/librealsense/blob/master/doc/distribution_linux.md#installing-the-packages) - In that case treat yourself as a developer. Make sure you follow the instructions to also install librealsense2-dev and librealsense-dkms packages.
#### OR
- #### Build from sources by downloading the latest [Intel® RealSense™ SDK 2.0](https://github.com/IntelRealSense/librealsense/releases/tag/v2.24.0) and follow the instructions under [Linux Installation](https://github.com/IntelRealSense/librealsense/blob/master/doc/installation.md)
### Step 2: Install the ROS distribution
- #### Install [ROS Kinetic](http://wiki.ros.org/kinetic/Installation/Ubuntu), on Ubuntu 16.04
### Step 3: Install Intel® RealSense™ ROS from Sources
- Create a [catkin](http://wiki.ros.org/catkin#Installing_catkin) workspace
```bash
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src/
```
- Clone the latest Intel® RealSense™ ROS from [here](https://github.com/intel-ros/realsense/releases) into 'catkin_ws/src/'
```bashrc
git clone https://github.com/IntelRealSense/realsense-ros.git
cd realsense-ros/
git checkout `git tag | sort -V | grep -P "^\d+\.\d+\.\d+" | tail -1`
cd ..
```
- Make sure all dependent packages are installed. You can check .travis.yml file for reference.
- Specifically, make sure that the ros package *ddynamic_reconfigure* is installed. If *ddynamic_reconfigure* cannot be installed using APT, you may clone it into your workspace 'catkin_ws/src/' from [here](https://github.com/pal-robotics/ddynamic_reconfigure/tree/kinetic-devel) (Version 0.2.0)
```bash
catkin_init_workspace
cd ..
catkin_make clean
catkin_make -DCATKIN_ENABLE_TESTING=False -DCMAKE_BUILD_TYPE=Release
catkin_make install
echo "source ~/catkin_ws/devel/setup.bash" >> ~/.bashrc
source ~/.bashrc
```
## Usage Instructions
### Start the camera node
To start the camera node in ROS:
```bash
roslaunch realsense2_camera rs_camera.launch
```
This will stream all camera sensors and publish on the appropriate ROS topics.
Other stream resolutions and frame rates can optionally be provided as parameters to the 'rs_camera.launch' file.
### Published Topics
The published topics differ according to the device and parameters.
After running the above command with D435i attached, the following list of topics will be available (This is a partial list. For full one type `rostopic list`):
- /camera/color/camera_info
- /camera/color/image_raw
- /camera/depth/camera_info
- /camera/depth/image_rect_raw
- /camera/extrinsics/depth_to_color
- /camera/extrinsics/depth_to_infra1
- /camera/extrinsics/depth_to_infra2
- /camera/infra1/camera_info
- /camera/infra1/image_rect_raw
- /camera/infra2/camera_info
- /camera/infra2/image_rect_raw
- /camera/gyro/imu_info
- /camera/gyro/sample
- /camera/accel/imu_info
- /camera/accel/sample
The "/camera" prefix is the default and can be changed. Check the rs_multiple_devices.launch file for an example.
If using D435 or D415, the gyro and accel topics wont be available. Likewise, other topics will be available when using T265 (see below).
### Launch parameters
The following parameters are available by the wrapper:
- **serial_no**: will attach to the device with the given serial number. Default, attach to available RealSense device in random.
- **rosbag_filename**: Will publish topics from rosbag file.
- **initial_reset**: On occasions the device was not closed properly and due to firmware issues needs to reset. If set to true, the device will reset prior to usage.
- **align_depth**: If set to true, will publish additional topics with the all the images aligned to the depth image.</br>
The topics are of the form: ```/camera/aligned_depth_to_color/image_raw``` etc.
- **filters**: any of the following options, separated by commas:</br>
- ```colorizer```: will color the depth image. On the depth topic an RGB image will be published, instead of the 16bit depth values .
- ```pointcloud```: will add a pointcloud topic `/camera/depth/color/points`. The texture of the pointcloud can be modified in rqt_reconfigure (see below) or using the parameters: `pointcloud_texture_stream` and `pointcloud_texture_index`. Run rqt_reconfigure to see available values for these parameters.</br>
The depth FOV and the texture FOV are not similar. By default, pointcloud is limited to the section of depth containing the texture. You can have a full depth to pointcloud, coloring the regions beyond the texture with zeros, by setting `allow_no_texture_points` to true.
- The following filters have detailed descriptions in : https://github.com/IntelRealSense/librealsense/blob/master/doc/post-processing-filters.md
- ```disparity``` - convert depth to disparity before applying other filters and back.
- ```spatial``` - filter the depth image spatially.
- ```temporal``` - filter the depth image temporally.
- ```hole_filling``` - apply hole-filling filter.
- ```decimation``` - reduces depth scene complexity.
- **enable_sync**: gathers closest frames of different sensors, infra red, color and depth, to be sent with the same timetag. This happens automatically when such filters as pointcloud are enabled.
- ***<stream_type>*_width**, ***<stream_type>*_height**, ***<stream_type>*_fps**: <stream_type> can be any of *infra, color, fisheye, depth, gyro, accel, pose*. Sets the required format of the device. If the specified combination of parameters is not available by the device, the stream will not be published. Setting a value to 0, will choose the first format in the inner list. (i.e. consistent between runs but not defined). Note: for gyro accel and pose, only _fps option is meaningful.
- **enable_*<stream_name>***: Choose whether to enable a specified stream or not. Default is true. <stream_name> can be any of *infra1, infra2, color, depth, fisheye, fisheye1, fisheye2, gyro, accel, pose*.
- **tf_prefix**: By default all frame's ids have the same prefix - `camera_`. This allows changing it per camera.
- **base_frame_id**: defines the frame_id all static transformations refers to.
- **odom_frame_id**: defines the origin coordinate system in ROS convention (X-Forward, Y-Left, Z-Up). pose topic defines the pose relative to that system.
- **All the rest of the frame_ids can be found in the template launch file: [nodelet.launch.xml](realsense2_camera/launch/includes/nodelet.launch.xml)**
- **unite_imu_method**: The D435i and T265 cameras have built in IMU components which produce 2 unrelated streams: *gyro* - which shows angular velocity and *accel* which shows linear acceleration. Each with it's own frequency. By default, 2 corresponding topics are available, each with only the relevant fields of the message sensor_msgs::Imu are filled out.
Setting *unite_imu_method* creates a new topic, *imu*, that replaces the default *gyro* and *accel* topics. Under the new topic, all the fields in the Imu message are filled out.
- **linear_interpolation**: Each message contains the last original value of item A interpolated with the previous value of item A, combined with the last original value of item B on last item B's timestamp. Items A and B are accel and gyro interchangeably, according to which type recently arrived from the sensor. The idea is to give the most recent information, united and without repetitions.
- **copy**: Fo
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码 机器人大赛参赛作品,供参赛人员参考,含设计文档,设计源码
资源推荐
资源详情
资源评论
收起资源包目录
中国机器人大赛通用比赛(gpsr).zip (2000个子文件)
test.abnf 368B
home_final.abnf 234B
MoveToPose.action 255B
HomeRecognize.action 132B
TTS.action 121B
robot_bnf.bnf 4KB
home.bnf 2KB
robot_home.bnf 2KB
gpsr_question_bak.bnf 2KB
GPSR_ONE.bnf 2KB
home_test.bnf 1KB
call.bnf 892B
GPSR.bnf 783B
gpsr_three.bnf 682B
home_generated_confirmed.bnf 631B
home_generated.bnf 546B
home_final.bnf 449B
home_final_bak.bnf 398B
supermarket.bnf 278B
guest.bnf 245B
region.c 501KB
lsd.c 44KB
data.c 44KB
parser.c 43KB
go.c 43KB
image.c 41KB
classifier.c 34KB
network.c 30KB
detector.c 28KB
lstm_layer.c 24KB
region.c 23KB
lock.c 21KB
region_layer.c 19KB
convolutional_layer.c 18KB
darknet.c 18KB
pf.c 16KB
attention.c 15KB
rnn.c 15KB
linuxrec.c 15KB
utils.c 14KB
gru_layer.c 13KB
nightmare.c 13KB
coco.c 13KB
yolo_layer.c 12KB
pf_kdtree.c 12KB
speech_recognizer.c 11KB
yolo.c 11KB
connected_layer.c 11KB
captcha.c 11KB
compare.c 11KB
demo.c 10KB
batchnorm_layer.c 10KB
detection_layer.c 10KB
rnn_layer.c 10KB
deconvolutional_layer.c 10KB
blas.c 9KB
crnn_layer.c 9KB
local_layer.c 9KB
box.c 8KB
gemm.c 8KB
cifar.c 8KB
segmenter.c 8KB
regressor.c 7KB
rnn_vid.c 7KB
pf_vector.c 6KB
eig3.c 5KB
normalization_layer.c 5KB
pf_pdf.c 5KB
cost_layer.c 5KB
reorg_layer.c 5KB
map_store.c 5KB
voxel.c 5KB
layer.c 4KB
writing.c 4KB
tag.c 4KB
matrix.c 4KB
pf_draw.c 4KB
cuda.c 4KB
map_draw.c 4KB
maxpool_layer.c 4KB
route_layer.c 4KB
tree.c 4KB
super.c 4KB
dice.c 4KB
activations.c 3KB
softmax_layer.c 3KB
upsample_layer.c 3KB
option_list.c 3KB
map_range.c 3KB
shortcut_layer.c 3KB
crop_layer.c 3KB
swag.c 2KB
logistic_layer.c 2KB
map.c 2KB
avgpool_layer.c 2KB
l2norm_layer.c 2KB
activation_layer.c 2KB
dropout_layer.c 2KB
art.c 1KB
list.c 1KB
共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20
资源评论
辣椒种子
- 粉丝: 3414
- 资源: 5723
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- vscode-1.64.1.tar源码文件
- vscode-1.64.0.tar源码文件
- vscode-1.52.0.tar源码文件
- Music-Player +PlayerActivity+ rockplayer+ SeeJoPlayer 播放器JAVA源码
- vscode-1.46.0.tar源码文件
- 最近很火植物大战僵尸杂交版2.08苹果+安卓+PC+防闪退工具V2+修改工具+高清工具+通关存档整合包更新
- 超级好用的截图工具PixPin,可录制Gif图
- Screenshot_2024-05-21-17-06-42-64_2332cb9b27b851b548ba47a91682926c.jpg
- 毕业设计参考 - 基于树莓派、OpenCV及Python的人脸识别
- node-v18.20.2-linux-arm64
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功