My new pySLAM v2 is out!
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:
- FAST
- Good features to track
- ORB
- ORB2 (improvements of ORB-SLAM2 to ORB detector)
- SIFT
- SURF
- KAZE
- AKAZE
- BRISK
- AGAST
- MSER
- StarDector/CenSurE
- Harris-Laplace
- SuperPoint
- D2-Net
- DELF
- Contextdesc
- LFNet
- R2D2
- Key.Net
The following feature descriptors are supported:
- ORB
- SIFT
- ROOT SIFT
- SURF
- AKAZE
- BRISK
- FREAK
- SuperPoint
- Tfeat
- BOOST_DESC
- DAISY
- LATCH
- LUCID
- VGG
- Hardnet
- GeoDesc
- SOSNet
- L2Net
- Log-polar descriptor
- D2-Net
- DELF
- Contextdesc
- LFNet
- R2D2
You can find more information in the file feature_types.py.
A serious benchmarking is still a work in progress.
Source code:
https://github.com/luigifreda/pyslam
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