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Computer vision for drone landing pads #1268
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Hey Josh, I saw you were assigned to #1267 as well - can you just focus on this task for now? |
Hey Cameron! Yeah, I can focus on that task for now.
…On Tue, Sep 10, 2024 at 5:06 PM cameron brown ***@***.***> wrote:
Hey Josh, I saw you were assigned to #1267
<#1267> as well - can you just focus
on this task for now?
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Status report: I attended the WAMV test on 10/16/24. I'll begin working on this issue soon. The plan for this, as suggested by @Keith-Khadar , is to create the landing pads in gazebo and develop a machine learning model based on that. I have the needed landing pad logos from RoboNation and the landing pad dimensions from the manual. I will make a set of images of the landing pad gazebo models, then label them in label studio, and then train a MIL Yolo model off of those images. |
Status report: I was unable to complete the gazebo world in time. I did help the mechanical team make a stencil for the Robonation objects for Task 8 (relevant to this issue), but I did not get to make a data set nor machine vision model off of it unfortunately. I tried to help figure out the params.py for path planner, but due to using a VM I was unable to run Gazebo and Nviz effectively. |
What needs to change?
We will need to develop a computer vision model to detect the drone landing pads. @LesterBonilla found a camera that does automatic computer vision from some self-developed machine learning model, which we will try first. If that camera's built-in vision model does not perform at the level we desire, then we can develop our own ML vision model that will run directly on the drone's pi.
How would this task be tested?
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