I am excited to release pySLAM v2. The new version allows you to play with SLAM techniques, visual-odometry, keyframes, bundle-adjustment, feature-matching, and many modern local features (based on new Deep Learning approaches). It was a long work to make everything accessible from a single python environment but it was worth it!
Now, you can easily test how many modern local features perform and compare with respect to classical ones, within a pure feature matching application or a within a SLAM/Visual-odometry pipeline. At the present time, the following feature detectors are supported:
- Good features to track
- ORB2 (improvements of ORB-SLAM2 to ORB detector)
The following feature descriptors are supported:
- ROOT SIFT
- Log-polar descriptor
You can find more information in the file feature_types.py.
A serious benchmarking is still a work in progress.
Check also my recent tweet with people’s comments, etc.
I am excited to release pySLAM v2. Now you can play with #SLAM techniques, #VisualOdometry, #Keyframes, #BundleAdjustment, #FeatureMatching, and many modern #LocalFeatures (based on DL). Everything is accessible from a single #python environment. https://t.co/XEPiuvnXJG pic.twitter.com/6K5yi5Z2G1
— Luigi Freda (@LuigiFreda) May 4, 2020