Comments (3)
Hello @EugenKarasev, thanks for your interest in our work.
how you found 7 points on the cone
We developed a dedicated neural network to detect the 7 keypoints on the cone: https://github.com/cv-core/MIT-Driverless-CV-TrainingInfra/tree/master/RektNet
How did we know the parameters of the calibrated camera?
We calibrated the camera in our own, manually.
How we know dimensions of the cone?
The shape of the cone is accessible through FSG's official competition manual, plus you can also measure it by yourself.
Implementation in the code
Pytorch Training: https://github.com/cv-core/MIT-Driverless-CV-TrainingInfra/tree/master/RektNet
TensorRT inference: https://github.com/cv-core/tensorrt_ros/blob/master/KeypointDetector.cpp
from mit-driverless-cv-traininginfra.
Yes, thank you.
But I am interested in exactly, how you determine the position of the cone?
from mit-driverless-cv-traininginfra.
Hi @EugenKarasev, this repo has all of the source files related to object detection. What you'd be looking for is a PnP solver for which there are multiple open-source implementations. You could try OpenCV's solvePnP
or solvePnPRansac
functions, alternatively this repo solves PnP using a lambda twist P3P solver which is supposed to be faster and more stable
from mit-driverless-cv-traininginfra.
Related Issues (13)
- RekNet for drone racing gates
- Cant Access Pretrained Weights
- How to download the datasets? HOT 10
- Dataset format HOT 3
- Whether the open source traffic cone data is not sorted by color?
- Classes
- RektNet Dataset HOT 1
- Implementing Perspective-n-Point in monocular depth estimation
- Unable to download RektNet dataset HOT 2
- DataParallel model stuck on loss.backward() HOT 1
- Label format - height is scaled with img width? HOT 1
- Labelled cone dataset. HOT 4
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