Reference material for the SlamDynamicConfig class / Is implementation of search_map_by_projection same as ORB-SLAM2?

[Mechazo11]

Hello @luigifreda

Could you provide some reference material for the SlamDynamicConfig class? It seems to be an important part of the pyslam pipeline but there is not much information about it.

Regarding the search_map_by_projection() method from the search_points.py file, this method alone is adding the highest amount of delay (on average 5 seconds). I was wondering whether the implementation is the same as that of ORB-SLAM2 (i.e ORB SLAM 1)?

With best,
@Mechazo11

[luigifreda]

Hi,
thanks for your feedback.

1. SlamDynamicConfig is helping in robustly estimating the descriptor distance standard deviation by using MAD.
   https://github.com/luigifreda/pyslam/blob/master/utils_features.py#L133
   Moreover, see for instance: https://en.wikipedia.org/wiki/Median_absolute_deviation

2. search_map_by_projection() is working in a very standard and well-known way. As for the performance, did you have a chance to read the main README
   "
   You can use this framework as a baseline to play with local features, VO techniques and create your own (proof of concept) VO/SLAM pipeline in python. When you test it, consider that's a work in progress, a development framework written in Python, without any pretense of having state-of-the-art localization accuracy or real-time performances.
   "
   As you may know, python is not the best language to write a real-time SLAM framework. It does not allow real multi-threading capabilities: for instance, see this discussion
   https://stackoverflow.com/questions/4496680/python-threads-all-executing-on-a-single-core
   All the python threads will actually run on a single core due to GIL limitations (assuming we are using the default CPython). A better approach could be achieved by using the multiprocessing modules. Clearly, there is always space for new improvements. Feel free to open a PR for a better implementation.