-
Notifications
You must be signed in to change notification settings - Fork 55
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
Discuss Semantic visual supported odometry #1
Comments
Thank you @AlejandroSilvestri for your support! I feel to be energised to push things forward. Any suggestions are welcome, the project itself is under evolving, I need more help. |
I saw many visual slam projects, and it seems to me those with good installation instructions were forked and cited more often than other. It is of great help if you always indicate if this is a work in progress, if the actual version is usable, and what to expect from it. A YouTube video always helps. |
@AlejandroSilvestri Sorry for the delay of reply. I am currently working on migrating cpp modules and plan to push them to the repository the next month. I understand that the installation is not very neat and I need to improve them. As suggested by @ausu0917, issue #2 gives me a good hint on strategies improving portability of the system by using docker. I am doing it now. I also figure out a plausible road map for the next steps. Proposed Roadmap1th PriorityMake a dockerfile and improve the installation system. How do you @AlejandroSilvestri think about it? 2nd PriorityMigrating cpp version of the system (I am currently working on it) and replace different models to make the job with a complete report (either good or bad). Finally, Transplanting to Semi-dense or Dense ProjectsWe have already observed that semantic features help in stability of matching, recognition and so on (though accuracy are affected by different factors such camera capturing speed, lighting ..., while people who working on ISP are helping us to get rid of them). Instead of using axis aligned bounding box, maybe more points produce better structure of objects (?). And better structure of points and matching gives us better results though PNP solvers. This means we can extend the work. FinallyI have learned too much from ORBSlam. I would like to take this opportunity to invite you help me improving the schedule and keep work in order. |
Yes! I believe containers are perfect encapsulation for this type of work demanding a lot of dependencies. |
Hi @yiakwy, good job!
It's not an easy task to build a new VO. I can see you are blending deep learning, one step toward Spatial AI (this is a term I read Davidson is installing to name the new visual slam generation with deep learning).
I opened this so called issue, inviting all those who want to join and discuss about this novel VO.
I believe Python is the best choice to deal with deep learning.
The text was updated successfully, but these errors were encountered: