I am looking for a indoor positioning/2d motion tracking system for small robot cars (1:8 scale RC cars). We want to use the system as a ground truth for the development of autonomous driving applications, so looking for an accuracy of a few millimeters. The testing area is around 10x10m.
The cars are running ROS (Robotic Operating System), so an existing implementation would be nice.
A known solution is a OptiTrack motion capture system, but with a cost of >10kEuro this is way above our budget of around 1.5kEuro. I have also been looking in using HTC Vive trackers with OpenVR, but I am not sure if this is a reliable solution.
Any idea would be very welcome!
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i am doing final year project as lane tracking using a camera. the most challenging task now is how i can measure distance between the camera (the car that carries it actually) and the lane.
While the lane is easily recognized (Hough line transform) but i found no way to measure distance to it.
given the fact that there is a way to measure distance to object in front of camera based on Pixel width of the object, but it does not work here be because the nearest point of the line, is blind in the camera.
What you want is to directly infer the depth map with a monocular camera.
You can refer my answer here
https://stackoverflow.com/a/64687551/11530294
Usually, we need a photometric measurement from a different position in the world to form a geometric understanding of the world(a.k.a depth map). For a single image, it is not possible to measure the geometric, but it is possible to infer depth from prior understanding.
One way for a single image to work is to use a deep learning-based method to direct infer depth. Usually, the deep learning-based approaches are all based on python, so if you only familiar with python, then this is the approach that you should go for. If the image is small enough, i think it is possible for realtime performance. There are many of this kind of work using CAFFE, TF, TORCH etc. you can search on git hub for more option. The one I posted here is what i used recently
reference:
Godard, Clément, et al. "Digging into self-supervised monocular depth estimation." Proceedings of the IEEE international conference on computer vision. 2019.
Source code: https://github.com/nianticlabs/monodepth2
The other way is to use a large FOV video for a single camera-based SLAM. This one has various constraints such as need good features, large FOV, slow motion, etc. You can find many of this work such as DTAM, LSDSLAM, DSO, etc. There are a couple of other packages from HKUST or ETH that does the mapping given the position(e.g if you have GPS/compass), some of the famous names are REMODE+SVO open_quadtree_mapping etc.
One typical example for a single camera-based SLAM would be LSDSLAM. It is a realtime SLAM.
This one is implemented based on ROS-C++, I remember they do publish the depth image. And you can write a python node to subscribe to the depth directly or the global optimized point cloud and project it into a depth map of any view angle.
reference: Engel, Jakob, Thomas Schöps, and Daniel Cremers. "LSD-SLAM: Large-scale direct monocular SLAM." European conference on computer vision. Springer, Cham, 2014.
source code: https://github.com/tum-vision/lsd_slam
My goal is to overlay material/texture on a physical object (it would be an architectural model) that I would have an identical 3d model of. The model would be static (on a table if that helps), but I obviously want to look at the object from any side. The footprint area of my physical models would tend to be no smaller than 15x15cm and could be as large as 2-3m^2, but I would be willing to change the size of the model to work with ARCore's capability.
I know ARCore is mainly designed to anchor digital objects to flat horizontal planes. My main question is, in its current state, is it capable of accompliahing my end goal? If i have this right, it would record physical point cloud data and attempt to match it to point cloud data of my digital model, then overlapping the two on the phone screen?
If that really isn't what ARCore is for, is there an alternative that I should be focusing on? In my head this sounded fairly straightforward, but I'm sure I'll get way out of my depth if I go about it an inefficient way. Speaking of depth, I would prefer not to use a depth sensor, since my target devices are phones.
I most definitely hope that it will be possible in the future - after all an AR toolkit without Computer Vision is not that helpful.
Unfortunately, according to the ARCore employee Ian, this is currently not directly supported but you could try to access the pixels via glReadPixels and then use OpenCV with these image bytes.
Quote from Ian:
I can't speak to future plans, but I agree that it's a desirable
capability. Unfortunately, my understanding is that current Android
platform limitations prevent providing a single buffer that can be
used as both a GPU texture and CPU-accessible image, so care must be
taken in providing that capability.
Updated: 25 September, 2022.
At the moment there's still no 3D Object Recognition API in ARCore 1.33.
But... You can use ML Kit framework and Augmented Images API (ARCore 1.2+) for some tasks.
According to Google documentation, you can use ARCore as input for Machine Learning models.
I want to use an already implemented SLAM algorithm for mapping my college campus.
I have found some algorithms on OpenSLAM.org and some other independent ones such as LSD-SLAM and Hector SLAM, which show some promises but they have limitation such as they use LIDAR, or don't extend to large dataset etc.
SLAM has been an active topic for many years and some groups have also mapped an entire town. Can someone point me to such efficient algorithm?
My requirements are:
It must use RGB camera/cameras.
Preferably produce (somewhat) dense map of area.
It should be able to map large area (I have seen some algo which can only map up to a desk table or room, but they usually lose track if there is jerk in camera motion (Observed in LSD SLAM) or take very few landmarks which is only useful for study purposes).
Preferably a ROS implementation.
can i use Kinect sensor to detect the motion of a robotic arm (KUKA LBR iiwa 7R800) and calculate it's links angles in order to make it control another robotic arm.
Of course this is possible but I don't think it is a good idea.
Suboptimal accuracy, lag due to processing of the 3d-data. I guess there are also cases where you cannot see all joints/links.
Kuka robots can output their joint angles directly. Use this data for synchronization or control both robots using the same external data.
Any measurement error might cause unwanted movements which in case of industrial robots can cause severe damage!
I'm a Software Engineering student in my last year in a 4-year bachelor degree program, I'm required to work on a graduation project of my own choice.
we are trying to find a way to notify the user of any thing the gets on his/her way while walking, this will be implemented as an android application so we have the ability to use the camera, we thought of Image processing and computer vision but neither me or any of my group members have any Image processing background, we searched a little bit and we found out about OpenCv.
So my question is do I need any special background to deal with OpenCv? and is it a good choice for the objective of my project to use computer vision, if not what alternatives do u advise me to use?
I appreciate your help.. thanks in advance!
At the first glance I would use 2 standard cameras to find depth image - stereo vision (similar to MS Kinect depth sensor)
from that it would be easy to fix a threshold to some distance.
Those algorithms are very CPU hungry so I do not think it will work on Android (although I have zero experience).
I you must use Android, I would look for some depth sensor (to avoid extracting depth data from 2 images)
For prototyping I would use MATLAB (or Octave), then I would switch to OpenCV (pointers, mem. allocations, blah...)