Utilized Passthrough and RANSAC filtering on the point-cloud data
Utilized Euclidean Clustering to distinguish identified objects for pick & place
roslaunch sensor_stick training.launch
rosrun sensor_stick capture_features.py
Output is training_set.sav file
rosrun sensor_stick train_svm.py
Output is Confusion Matrices and model.sav file
Test World 1
Confusion Matrix - Not Confused
3 of 3 Objects Identified
Output_1 YAML file included
- PCL documentation : http://strawlab.github.io/python-pcl/
- RANSAC algorithm : http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FISHER/RANSAC/
- Outlier Removal (paper) : http://people.csail.mit.edu/changil/assets/point-cloud-noise-removal-3dv-2016-wolff-et-al.pdf
- Clustering Algorithm : http://bit.ly/clustering-tutorial
- Segmentation with NN (intro) : http://bit.ly/segmentation-intro-nn