Open hellovuong opened 7 months ago
Also waiting for this critical feature. Cannot load full vocabulary due to huge memory needs to allocate and dont manage to get same accuracy while activating WM/LTM.
@alexk1976 You can try to use kp/NNStrategy
to 0 or 2. It doesn't need to uncompress the descriptor from bin to float to build vocabulary, this saves 50% of memory usage, and it goes with the reduced perform of Near Neighbor search of course. However, the need of this feature is still necessary.
Here some options:
Kp/MaxFeatures: 1500
is high, default is 500. You can fix your current database with:
rtabmap-reprocess --Kp/MaxFeatures 500 input.db output.db
You can try Kp/ByteToFloat: true
to save some RAM.
At the other extreme, we can use a fixed dictionary with less words (<1M): https://github.com/introlab/rtabmap/issues/942#issuecomment-1345657263
Thank you!
i dont think it's a real solution..a bit bigger area like we have and its impossible to load dictionary even when we set MaxFeatures=500. If we dont use FlanTree - have performance issues. Fixed dictionary - gives worse accuracy. We need a way to load full graph and only part of the dictionary
@alexk1976 Agreed, it is kinda included in that other issue https://github.com/introlab/rtabmap/issues/1201 .
Hello @matlabbe, We created a pretty good map of a large area (multi-session mapping: 5). It ended up with >4 million words in the vocabulary, which led to rtabmap crashing every time It ran in localization mode for minutes or when I called the backup service explicitly. I am aware that you already opened issue #1201, however, I am wondering if any intermediate step to reduce the size of words? Some parameters that may related for you to check:
Let me know if I can provide more information to help you with support.
Thank you for your contribution and hope that will receive your reply soon.