open_vins VS SuperGluePretrainedNetwork

From what I have understood after reading research papers related to the VSLAM, the modularity aspect is not easy to achieve given the extracted features and descriptors are intrinsically linked with feature matching and handling of map points. I would like to know if there are some good Open Source VSLAM projects available which can be used with different feature extractors so I can get a comparative results with respect to just changing the feature extractors . I have tried pyslam project which is actually quite good considering the modularity but as the author himself points out this is only for academic purposes and when I compared the results of ORB_SLAM2 feature extractor using this module vs the original ORB_SLAM2 for KITTI data set , the results are not comparable. I am also looking into OpenVINS ( and from initial reading it is also using ORB Features, although it does have a base Tracker class which can be modified to create a new Tracker with different descriptor) If anyone has worked with custom feature extractor incorporated into prebuilt SLAM pipeline and can guide me as to how to proceed with the implementation of custom Feature extractor into a SLAM Front end using a Open Source VSLAM framework, it will be really helpful.

Because the standard MSCKF is the only one that doesn't contain the map points in the state. Note that this is only for the standard MSCKF. More modern MSCKFS variations like OpenVINS will actually add some SLAM features because it improves the accuracy.

Aside from that there have been some publications of some high quality open source SLAM systems like OpenVINS and ORB-SLAM3.

Check out open vins. Its an implementation of the vins slam project. https://github.com/rpng/open_vins

Generally you want to use a Kalman Filter based method if you want access to the uncertainties. This is because it is much easier to extract a subset of the covariance in the kalman filter form. I would recommend OpenVins. One of the best open source visual odometry projects, and it is pretty well documented.

Code: https://github.com/magicleap/SuperGluePretrainedNetwork

This basically includes a deep learning based approach to do keypoint detection, and match them across image frames. This includes papers like SuperPoint, Superglue, and more. There is also a way to do dense matching with neural networks.

Feature matching is the way to go imo. Try out OpenCV's inbuilt feature matching methods like SIFT and FLANN. If the performance is poor, you can even try out CNN aided matching algos like SuperGlue Link (CVPR2020)

SIFT and brute force matching is your best bet in classical computer vision if you're unconcerned with runtime. There are methods from deep learning that can perform better, somewhat domain dependent. Check out superpoint and superglue from magic leap. https://github.com/magicleap/SuperGluePretrainedNetwork