Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

GPU-accelerated obstacle detection #620

Merged
merged 229 commits into from
Feb 20, 2024
Merged

GPU-accelerated obstacle detection #620

merged 229 commits into from
Feb 20, 2024

Conversation

jbrhm
Copy link
Contributor

@jbrhm jbrhm commented Dec 3, 2023

Summary

Closes #597 #601

What features did you add, bugs did you fix, etc?
I added Tensor RT in order to run the the object detection code. Then, using the data collected I locate the object using the point cloud. Additionally, this data is published to the tf tree under "detectedobject" and heading information is published under the "/object_detector/detected_object" topic.

Did you add documentation to the wiki?

No I'm not sure how to do this

How was this code tested?

I ran the ZED_test.launch file and set the map base to the zed camera frame. Then I rosran the objected_detector_node and the algorithm correctly identified the object in 3D space.

Here is a video of the setup running https://youtu.be/vapBOepGkZY

Did you test this in sim?

Yes/No
No

Did you test this on the rover?

Yes/No
No

Did you add unit tests?

Yes/No (If not explain why not)
No

@jbrhm jbrhm merged commit 79291b5 into integration Feb 20, 2024
1 check passed
@jbrhm jbrhm deleted the percep/obj-detect branch February 20, 2024 03:19
@qhdwight
Copy link
Collaborator

You beast

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

Successfully merging this pull request may close these issues.

Create our own parser for yolov8 DNN output