![axis camera station yellow dot axis camera station yellow dot](https://i.ytimg.com/vi/8Ko2xVy_fwQ/maxresdefault.jpg)
- #Axis camera station yellow dot how to
- #Axis camera station yellow dot code
- #Axis camera station yellow dot series
We then manually try to locate the pixel point u=628 and v=342:Īnd we input this manually into the perspective calibration: #ENTER (X,Y,d*) If you refer to the pinhole model, these are equivalent to u and v pixel values.įrom our intrinsic calibration, we obtain Cx and Cy The first step, is to identify the Cx, Cy and z values for the camera, and we use the New Camera Matrix to find that Cx=628 and Cy=342.
#Axis camera station yellow dot series
To get to this point, It involved a series of more steps to get it to work reliably, which I will explain. In my setup, I’m using a single fixed camera, which means that once we calibrate for the perspective, the model should begin to work. Getting the Perspective Calibration Right You can find the Python script for this Initial calibration here. I ended up using around 40 images for calibration and learned that in order to improve the “stability” of the scaling factor (s) I need to position the chessboard in the same plane as where I wanted the detection of X Y Z. I used the “Undistort” preview to check my work and I found that the undistortion pattern had a lot of variation as follows: Variation of the un-distort function based on images and poses used.
#Axis camera station yellow dot how to
Chessboard Calibrationįollowing the Chessboard calibration example, I believe the recommendation is to use 10 or more images and it provides no clarification on how to “pose” the chessboard. I followed the steps from the OpenCV Camera Calibration and even used a lot of the example code, but I did find something interesting. The first step to calibrate your setup, is to find what is called the intrinsic parameters of your camera, which are based on how the camera is build and one of the key factors to calibrate, is the distortion that is caused by the curvature of the camera lens. Getting Intrinsic Camera Calibration Right This was a crucial step that enable me to get to a working solution and while working through this another interesting aspect popped-up. I was looking for this, and I couldn’t find any references that could easily explain how to do it:
![axis camera station yellow dot axis camera station yellow dot](https://www.menschen-brauchen-menschen.org/wp-content/uploads/2021/06/Bildung_Entwicklung450x450px_b.png)
#Axis camera station yellow dot code
This illustration will be crucial to understand the code and how you can use it on your own projects (which may have different frames of reference based on your application). Red is the X-axis, Green the Y-Axis and Blue the Z-axis, the arrows point in the direction of Positive increases. You can read through my Medium post on the overview of the robot and watch the video of it in operation in Youtube.Īs a reminder, this is the setup of this robot: Horizontal Travel Robot Arm – HTA0Īs I dive deeper in this blog, it will be very important to keep in mind the frames of references I’ve used: Robot, Camera and Plan coordinate frames of reference I’m counting you will refer to the code repository as well as the multiple diagrams I reference below. Note: I’ve taken the liberty of highlighting what I consider the most important parts. Just in case you want to dive right in, you can access the code via my Github HTA0 robot project. As soon as I finished my Horizontal Travel Robot Arm prototype and was able to reliable make pick and place motions using simple X, Y, Z inputs, I decided to build a real use case that could show it’s potential for real world applications.Įnabling the robot to have Computer Vision seems like a very straightforward case, and I learned a lot that I want to share, as most likely you will find it useful.