This article serves as an introduction to the Intel Reansense D400 series RGB-D cameras. It details the SDK installation process, ROS intergration, sensor calibration and sensor tuning. Following the intructions in the SDK and the ROS Package section, readers should be able to launch the sensor through ROS and browse through the published topics. The calibration and tuning sections of the articles allow advanced users to ensure the best sensor reading quality.
Intel® RealSense™ D400 Series Depth Cameras are small, easily-interfaced multifunctional camera sensors, which provide various sensor functionalities, such as RGB camera view, depth camera view, fisheye camera view, infrared camera view, along with calibration information and inertial data.
The RealSense camera package allows access to and provides ROS nodes for Intel 3D cameras and advanced modules. The SDK allows depth and color streaming, and also provides camera calibration information. The source code can be downloaded and built from this repository: https://github.com/IntelRealSense/librealsense/
Step 1: Install the latest Intel® RealSense™ SDK 2.0 Install from Debian Package - In that case treat yourself as a developer. Make sure you follow the instructions to also install librealsense2-dev and librealsense-dkms packages.
Build from sources by downloading the latest Intel® RealSense™ SDK 2.0 and follow the instructions under Linux Installation
Step 2: Install the ROS distribution (ROS Kinetic, on Ubuntu 16.04)
Step 3: Install Intel® RealSense™ ROS from Sources
Create a catkin workspace
mkdir -p ~/catkin_ws/src cd ~/catkin_ws/src/
Clone the latest Intel® RealSense™ ROS from here into ‘catkin_ws/src/’
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 (Version 0.2.0)
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
Start Camera Node
roslaunch realsense2_camera rs_camera.launch Will start the camera node in ROS and stream all camera sensors.
Published ROS 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):
The intrinsics of the camera module can be found in the /camera/color/camera_info topic when the realsense camera is launched through ROS. The K matrix in the published topic corresponds to the intrinsics matrix.
The intrinsic of the camera module can be calibrated with the matlab single camera calibration App. www.mathworks.com/help/vision/ug/single-camera-calibrator-app.html
The general steps are as follows:
Prepare images, camera, and calibration pattern.
Add images and select standard or fisheye camera model.
Calibrate the camera.
Evaluate calibration accuracy.
Adjust parameters to improve accuracy (if necessary).
Export the parameters object.
Note: The checkerboard required can be generated on this site https://calib.io/pages/camera-calibration-pattern-generator. After printing out the checkerboard, make sure to measure the box sizes and verify the print accuracy.Printers can be very inaccurate sometimes. Remember to stick the checkerboard on a piece of flat cardboard. When taking pictures of the checkerboard, make sure to take the pictures at different angle and different distant.
The realsense sensors are calibrated out of the box. However, if the sensor was dropped or there is a high amount of vibration in your application it might be worthy to recalirate the extrinc of the sensor for better depth map quality. The extrinsic here refers to the coordinate transformation between the depth modules and the camera on the sensor. For this task, intel has developed a dynamic calibration tool(https://www.intel.com/content/dam/support/us/en/documents/emerging-technologies/intel-realsense-technology/RealSense_D400_Dyn_Calib_User_Guide.pdf).
Two types of dynamic calibrations are offered in this tool:
Rectification calibration: aligning the epipolar line to enable the depth pipeline to work correctly and reduce the holes in the depth image
Depth scale calibration: aligning the depth frame due to changes in position of the optical elements
Tuning and Sensor Characteristics
The depth image precision is affected by the output resolution. The optimal resolutions of the D430 series are as follow:
- D415: 1280 x 720
- D435: 848 x 480
Lower resolutions can be used but will degrade the depth precision. Stereo depth sensors derive their depth ranging performance from the ability to match positions of objects in the left and right images. The higher the input resolution, the better the input image, the better the depth precision.
If lower resolution is needed for the application, it is better to publish high resolution image and depth map from the sensor and downsample immediately instead of publishing low resolution image and depth map.
Check whether auto-exposure works well, or switch to manual exposure to make sure you have good color or monochrome left and right images. Poor exposure is the number one reason for bad performance.
From personal experience, it is best to keep auto-exposure on to ensure best quality. Auto exposure could be set using the intel realsense SDK or be set in the realsense viewer GUI.
There are two other options to consider when using the autoexposure feature. When Autoexposure is turned on, it will average the intensity of all the pixels inside of a predefined Region-Of-Interest (ROI) and will try to maintain this value at a predefined Setpoint. Both the ROI and the Setpoint can be set in software. In the RealSense Viewer the setpoint can be found under the Advanced Controls/AE Control.
The ROI can also be set in the RealSense Viewer, but will only appear after streaming has been turn on. (Ensure upper right switch is on).
- D400 depth cameras give most precise depth ranging data for objects that are near. The depth error scales as the square of the distance away.
- However, the depth camera can’t be too close to the object that it is within the minz distance. The minZ for the D415 at 1280 x 720 is 43.8cm and the minz for the D435 at 848x480 is 16.8cm
The realsense SDK offers a range of post processing filters that could drastically improve the quality. However, by default, those filters aren’t enabled. You need to manually enable them. To enable the filters, you simply need to add them to your realsense camera launch file under the filters param https://github.com/IntelRealSense/realsense-ros#launch-parameters. The intel recommended filters are the following:
Sub-sampling: Do intelligent sub-sampling. We usually recommend doing a non- zero mean for a pixel and its neighbors. All stereo algorithms do involve someconvolution operations, so reducing the (X, Y) resolution after capture is usually very beneficial for reducing the required compute for higher-level apps. A factor of 2 reduction in resolution will speed subsequent processing up by 4x, and a scale factor of 4 will decrease compute by 16x. Moreover, the subsampling can be used to do some rudimentary hole filling and smoothing of data using either a non-zero mean or non-zero median function. Finally, sub-sampling actually tends to help with the visualization of the point-cloud as well.
Temporal filtering: Whenever possible, use some amount of time averaging to improve the depth, making sure not to take “holes” (depth=0) into account. There is temporal noise in the depth data. We recommend using an IIR filter. In some cases it may also be beneficial to use “persistence”, where the last valid value is retained indefinitely or within a certain time frame.
Edge-preserving filtering: This will smooth the depth noise, retain edges while making surfaces flatter. However, again care should be taken to use parameters that do not over-aggressively remove features. We recommend doing this processing in the disparity domain (i.e. the depth scale is 1/distance), and experimenting by gradually increasing the step size threshold until it looks best for the intended usage. Another successful post-processing technique is to use a Domain-Transform filter guided by the RGB image or a bilinear filter. This can help sharpen up edges for example.
Hole-filling: Some applications are intolerant of holes in the depth. For example, for depth-enhanced photography it is important to have depth values for every pixel, even if it is a guess. For this, it becomes necessary to fill in the holes with best guesses based on neighboring values, or the RGB image.