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

ORB-SLAM dies after 5 minutes #49

Closed
MarcGyongyosi opened this issue Aug 4, 2015 · 7 comments
Closed

ORB-SLAM dies after 5 minutes #49

MarcGyongyosi opened this issue Aug 4, 2015 · 7 comments

Comments

@MarcGyongyosi
Copy link

Hi,

when using ORB-SLAM in our applications (onboard an MAV) everything works fine until about 5 minutes into flight. Usually around that time, the ORB-SLAM process dies. Is there a way to see/check why it dies? Is it a memory issue? It just gives us exit code -9

@raulmur
Copy link
Owner

raulmur commented Aug 4, 2015

Hi,
Maybe the system runs out of memory and the OS kills the process. Can you check the memory consumption when you are running the application? How much RAM do you have in the MAV?

@MarcGyongyosi
Copy link
Author

Hi thanks for getting back. I will check the memory consumption during runtime when i get back to the lab later today. I certainly know that I have 2Gb RAM onboard.

@raulmur
Copy link
Owner

raulmur commented Aug 4, 2015

Ok, 2Gb is extremely little RAM for ORB-SLAM. The current way to load the ORB vocabulary through cv::FileStorage requieres a bit less than 2Gb (this could be done much more efficiently, but it would requiere you to modify the save/load vocabulary functions of DBoW2). Another option is to use a smaller vocabulary ( 5 levels and10 nodes per level, should work fine as well, currently we are using 6 levels).

@MarcGyongyosi
Copy link
Author

Ok thanks, I created a smaller vocabulary and it seems to do better now - it's been running for about 7 minutes so far, no problem yet! I was wondering if you could elaborate on what you consider a "large" set of images (as you write in the paper). What number of images do you recommend for a fixed indoor environment? And do you recommend those pictures only be taken in the respective indoor environment or does including pictures from other places improve tracking accuracy?

Thanks!

Marc

@raulmur
Copy link
Owner

raulmur commented Sep 1, 2015

Check the new update of the code. We load the vocabulary in a more efficient way and now it requires only about 350Mb.

@MarcGyongyosi
Copy link
Author

I will check this out next week, just got back from vacation. Thanks for publishing the modification!

@MarcGyongyosi
Copy link
Author

yes, it works much better now. Thanks for making this fix available!

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

No branches or pull requests

2 participants