I'm trying to calibrate my camera. A question came to me that if the picture had higher resolution what would happen. I think that the coordinates of the joints would be more accurate with respect to the center of the picture. Is there any other effects?
Related
I just added a sprite-circle to a 2D-Game with physics. I just realized that the circle has only very few vertices. Can I increase the vertex count of the circle by using GUI only?
I am using the LTS 2020.3.29f1 version of Unity
A sprite does not have vertices, it has pixels.
You can use a higher resolution texture if you wish to make the sprite look better when scaled up.
If your concern is accuracy of the collider's hitbox, then you can't do better than the CircleCollider2D as it has effectively infinite resolution in respect to a perfect circle.
I made a mesh from a Digital Elevation Map that spanned 1x1 degree box of geography, but when I scale the mesh up to 11139m in blender I get these visible jagged shadows on the peaks of the mesh. I'd prefer to not scale everything down but I suppose I can, it just seems like a strange issue I want to better understand.
My goal is to use the landscape in a WebVR application, but when I put this mesh into an Aframe scene it also has this issue. Thanks for any tips!
Quick answer:
I think this may be caused by the clipping start/end values. Also called near/far clipping planes. Adjusting them may fix the issue but also limit the rendering distance.
Longer explanation:
Take a look at this:
It's a simple grayscale, but imagine it is scaled across your entire scene depth (Z depth buffer). The range of this buffer is set by the start/stop clipping (near/far) camera setting.
By default Blender has its start/stop (near/far) clipping set to 0.01 - 1000.
While A-Frame has it like 0.005 - 10000. You may find more information here: A-Frame camera #properties
That means the renderer has to somehow fit every single point in that range somewhere on the grayscale. That may cause overlapping or Z-fighting because it is simply lacking precision to distinguish the details. And that is mainly visible at edges/peaks because the polygons are connected there at acute angles and the program has to round up the Z-values. That causes overlapping visible as darker shadows (most likely the backside of the polygon behind).
You may also want to read more about Z-fighting because it is somewhat related.
Example
below you can see the result of the optical flow if a camera makes a translation movement. If the camera makes a roll rotation the result looks like the second picture. Is it possible to retrieve the yaw angle from a camera if its only rotation around the yaw axis?
I think in the optical flow you can recognize if the camera is rotating around the yaw axis (z-axis), but i don't know how to retrieve the information how much the cam has rotated.
I would be gradeful for any hints. Thanks
Translation:
Roll rotation:
Orientation of camera:
If you have a pure rotation of your cam then you can use findhomography. You need four point correspondence in your pictures. For a pure rotation the homography matrix is already a rotation matrix. Otherwise you need to decompose the homograohy matrix. For a camera movement off 6 dof you can use the function find essential matrix and decompose this to translation and rotation.
I've found this beautiful quick way to remove gravity from accelerometer readings. However, I have a 6dof IMU (xyz gyro, xyz accel, no magnetometer) so I am not sure if I can use this code (I tried and it doesn't work correctly).
How would someone remove the gravity component? It's a big obstacle because I can't proceed with my project.
EDIT:
What I have:
quaternion depicting the position of aircraft (got that using Extended Kalman Filter)
acceleration sensor readings (unfiltered; axes aligned as the plane is aligned; gravity is also incorporated in these readings)
What I want:
remove the gravity
correct (rotate) the accelerometer readings so it's axes will be aligned with earth's frame of reference's axes
read the acceleration towards earth (now Z component of accelerometer)
Basically I want to read the acceleration towards earth no matter how the plane is oriented! But first step is to remove gravity I guess.
UPDATE: OK, so what you need is to rotate a vector with quaternion. See here or here.
You rotate the measured acceleration vector with the quaternion (corresponding to the orientation) then you substract gravity [0, 0, 9.81] (you may have -9.81 depending on your sign conventions) from the result. That's all.
I have implemented sensor fusion for Shimmer 2 devices based on this manuscript, I highly recommend it. It only uses accelerometers and gyroscopes but no magnetometer, and does exactly what you are looking for.
The resource you link to in your question is misleading. It relies on the quaternion that comes from sensor fusion. In other words, somebody already did the heavy lifting for you, already prepared the gravity compensation for you.
I have a robot and a camera. The robot is just a 3D printer where I changed the extruder for a tool, so it doesn't print but it moves every axis independently. The bed is transparent, and below the bed there is a camera, the camera never moves. It is just a normal webcam (playstation eye).
I want to calibrate the robot and the camera, so that when I click on a pixel on a image provided by the camera, the robot will go there. I know I can measure the translation and the rotation between the two frames, but that will probably return lots of errors.
So that's my question, how can I relate the camera and a robot. The camera is already calibrated using chessboards.
In order to make everything easier, the Z-axis can be ignored. So the calibration will be over X and Y.
It depends of what error is acceptable for you.
We have similar setup where we have camera which looks at some plane with object on it that can be moved.
We assume that the image and plane are parallel.
First lets calculate the rotation. Put the tool in such position that you see it on the center of the image, move it on one axis select the point on the image that is corresponding to tool position.
Those two points will give you a vector in the image coordinate system.
The angle between this vector and original image axis will give the rotation.
The scale may be calculated in the similar way, knowing the vector length (in pixels) and the distance between the tool positions(in mm or cm) will give you the scale factor between the image and real world axis.
If this method won't provide enough accuracy you may calibrate the camera for distortion and relative position to the plane using computer vision techniques. Which is more complicated.
See the following links
http://opencv.willowgarage.com/documentation/camera_calibration_and_3d_reconstruction.html
http://dasl.mem.drexel.edu/~noahKuntz/openCVTut10.html