Stereo image sensing technologies use two cameras to calculate depth and enable devices to see, understand, interact with, and learn from their environment. Depth cameras in the Intel RealSense D400 family work both indoors and outdoors in a wide variety of lighting conditions and can also be used in multiple camera configurations without the need for custom calibration.
I mean, filters designed to fit in front the cameras well. I can get an UV filter, but it is a circle-shaped and there is a possibility that the sunlight will penetrate betwen the filter and sensor. That can be a serious problem.
The end of Realsense cameras is a real disappointment for us as we use D435s extensively in our robots. We are definitely working on finding a replacement but none so far which match the following points:
I have had good success with the Asus Xtion using the openni2 drivers in ROS1. This is similar in cost ($300 US)to the Oak-D camera. The Xtion is the unit used by TRI on the head camera for the HSR and Stanford on the head camera for Jackrabbot2. The Xtion is limited to indoor and close (0-3m) range. I still would like to see a side by side comparison, of not only the cameras mentioned, but also other depth and stereo cameras. I understand that besides the sensor, the computing overhead of using optical sensors fro both SLAM and object recognition and tracking, is also a major consideration.
Compared to many other cameras it has a compute power that does not drag a significant compute power from the host machine. This was a selling point of Realsense at least for me as we could use on embedded ARM machines.
The core of the system, the unique Intel RealSense Vision Processor D4, uses advanced algorithms to process raw image streams from the depth cameras and computes high resolution 3D depth maps without the need for dedicated GPU or host processor. A variety of depth modules and housed camera devices provide an easy solution for rapid integration into industrial vision systems.
Intel RealSense technology supports a wide range of operating systems and programming languages. The Intel RealSense SDK enables you to extract depth data from the camera and use the interpretation of this data in the platform of your choice, including Intel and ARM processors. Developer tools are available in Windows OS, Linux, Mac OS, and more. The kit also offers sample code, debug tools, and evaluation tools to accelerate your project.
Choose between board level module configurations for higher volume applications and housed camera modules. D400 series depth modules for custom high volume embedded applications are available with either a rolling or global shutter with Full HD resolution. All modules consist of a stereo camera pair with optional pattern projector and optional HD colour camera. Developers can omit the projector and RGB HD camera to reduce costs where the application does not require these features. For evaluation, development and lower volume applications, the two aluminium housed D400 cameras are ideal.
The RPI4 supports USB3 which allows both pose + image data to be captured from the camera. The slower RPI3 only has USB2 meaning only pose data can be captured although this is sufficient for most users.
The Realsense T265 is supported via librealsense on Windows and Linux. Installation process varies widely for different systems, hence refer to the official github page for instructions for your specific system:
For RPi running Ubuntu, the installation process for librealsense has been detailed in this wiki. Follow the instructions to install librealsense and pyrealsense2. Since we are not using ROS, realsense-ros is not required.
Before the script can be run, the PYTHONPATH environment variable needs to be added with the path to the pyrealsense2 library. Alternatively, copy the build output (librealsense2.so and pyrealsense2.so in /librealsense/build/) next to the script. First, run the test script t265_test_streams.py to verify installation of pyrealsense2 and the T265 is connected.
The RealSense product is made of Vision Processors, Depth and Tracking Modules, and Depth Cameras, supported by an open source, cross-platform SDK, simplifying supporting cameras for third party software developers, system integrators, ODMs and OEMs.
As of January 2018, new Intel RealSense D400 Product Family was launched with the Intel RealSense Vision Processor D4, Intel RealSense Depth Module D400 Series, and 2 ready to use depth cameras: Intel RealSense Depth Cameras D435 and D415.
Previous generations of Intel RealSense depth cameras (F200, R200 and SR300) were implemented in multiple laptop and tablet computers by Asus, HP, Dell, Lenovo, and Acer. Additionally, Razer and Creative offered consumer ready standalone webcams with the Intel RealSense camera built into the design.: Razer Stargazer and the Creative BlasterX Senz3D.
The Intel RealSense Depth Module D400 Series is designed for easy integration to bring 3D into devices and machines. Intel also released the D415 and D435 in 2018. Both cameras feature the RealSense Vision processor D4 and camera sensors. They are supported by the cross-platform and open source Intel RealSense SDK 2.0. The Intel D415 is designed for more precise measurements.
This is a stand-alone camera that can be attached to a desktop or laptop computer. It is intended to be used for natural gesture-based interaction, face recognition, immersive, video conferencing and collaboration, gaming and learning and 3D scanning. There was also version of this camera to be embedded into laptop computers.
Snapshot is a camera intended to be built into tablet computers and possibly smartphones. Its intended uses include taking photographs and performing after the fact refocusing, distance measurements, and applying motion photo filters. The refocus feature differs from a plenoptic camera in that RealSense Snapshot takes pictures with large depth of field so that initially the whole picture is in focus and then in software it selectively blurs parts of the image depending on their distance. The Dell Venue 8 7000 Series Android tablet is equipped with this camera.
Rear-mounted camera for Microsoft Surface or a similar tablet, like the HP Spectre X2. This camera is intended for augmented reality applications, content creation, and object scanning. Its depth accuracy is on the order of millimeters and its range is up to 6.0 meters. The R200 is a stereo camera and is able to obtain accurate depth outdoors as well as indoors.
However, after speaking with some of our industry sources to try and get a better sense of what happened, we learned that what's actually going on might be more nuanced. And as it turns out, it is: Intel will continue to provide RealSense stereo cameras to people who want them for now, although long term, things don't look good.
Hmm. The very careful wording here suggests some things to me, none of them good. The \"RealSense business\" is still being wound down, and while Intel will \"continue to provide\" RealSense cameras to customers, my interpretation is that they're still mostly doing what they said in their first release, which is moving their focus and talent elsewhere. So, no more development of new RealSense products, no more community engagement, and probably a minimal amount of support. If you want to buy a RealSense camera from a distributor, great, go ahead and do that, but I wouldn't look for much else. Also, \"continue to provide\" doesn't necessarily mean \"continue to manufacture.\" It could be that Intel has a big pile of cameras that they need to get rid of, and that once they're gone, that'll be the end of RealSense.
As we want to have the camera model visible with the rover model, we need to change robot.urdf.xacro file in /etc/ros/urdf directory. On this site you can see what RealSense urdf camera models are available in the realsense2_description package. Each of the urdf files there has xacro macro with a lot of properties. You have to include such macro in the robot.urdf.xacro file on the Rover, and change name property to avoid tf frame conflicts. To do so, you need to include such lines
You can specify name property to anything you want. We just need to change it, so we don't have conflicts on camera_link tf frame. Origin property is for specifying the position of the camera regarding the link specified in parent property. So if the parent property is set to \"base_link\", the position in origin property is regarding the origin of the Rover.
You can also start rviz, and add robot model in the display view. Chose also base_link as fixed frame, and you should see the rover model with camera attached.
Stereo cameras are commonly used in projects involving autonomous navigation, you might be interested in a tutorial about it. They are however, not the only way of teaching a Leo Rover how to move on it's own. Check out our line follower tutorial if you want to learn more. You can also check our Knowledge Base for more instructions.
We've been talking about Intel's RealSense 3D depth-sensing cameras for years, but devices with the tech inside finally made their debut in mainstream products at CES 2015. With more than half a dozen PCs and tablets from the likes of Acer, Dell, Lenovo and HP now shipping with the technology and more devices to come, a lot of consumers and business users will be getting systems with RealSense this year. But just what does it do Here's a quick guide.
RealSense cameras feature three lenses, a standard 2D camera for regular photo and video, along with an infrared camera and an infrared laser projector. The infrared parts allow RealSense to see the distance between objects, separating objects from the background layers behind them and allowing for much better object, facial and gesture recognition than a traditional camera. The devices come in three flavors: front-facing, rear-facing and snapshot. 59ce067264