stella-cv / fbow_orb_vocab Goto Github PK

I'm doing some research into what effect the choice of vocabulary has on the efficacy of SLAM in a given environment, and trying to train my own vocabularies to see if this yields different results for a project I'm involved with.

When researching online, it seems that most users are using the default vocabulary files that come with the software (either OpenVSLAM, or a different package), and I haven't seen much discussion that gives a white-box view as to how those vocabularies have been generated - except that lotso f images have been used and so users should expect superior results to generating their own. However, I would expect that this requires the environment in which SLAM is being used to represent the environment used for the vocabulary - indoor explaration of corridors in a building, or outdoor exploration of a city, shopping centre, etc.

It would help to have some information in the readme, so I know what it is I am comparing against, and what environment it was designed for. Some ideas include:

• What sort of images (and if a specific available dataset, where this is located) were used to generate the vocabulary,
• How many, or what subset, of this dataset was used to create the vocabulary,
• At what resolutions, or if any other transformations were applied,
• Noting the k-L parameters used,
• Any other settings (e.g. parameters for ORB itself)
• (Also helpful for reference) how long it took to create, to help judge the computational expense of creating a vocabulary

Thanks!!

Hi,
Which ORB SLAM algorithm is this following, Is this ORB SLAM2 or ORB SLAM3?