ROS affordance_learning stack: perception v1.0

I’ve finally finished the very first version of perception component of the affordance_learning stack.

Perception module is mainly consisted of perceptors, and each perceptor includes specialized feature_extractor(s) depending on the problem, or salient features that we’ve designed. Finally, there are object(s) that each feature_extractor processes. Objects can be thought as if they are the salient parts of the environment that robot -somehow- extracts from the acquired raw sensory data.

Below is the UML diagram which shows the the slightly earlier version of the architecture to some extent.

Some part of the affordance_learning perception (al::perception) module.

There are several issues that I see in the current system:

  1. OBject identification checks the similarity of the clusters according to their bounding box center change in one perception cycle, that’s in the video below id of the cluster 4 becomes 1 since the object displaced more than a prespecified threshold. This can be improved by using a more complicated similarity check (e.g. one that considers bounding box dimensions, and pose).
  2. SurfaceFeatureExtractor class only extracts surface normals, and the parameters are better be tuned for a more descriptive performance.
  3. Matlab subplots are not shown in order, not really a problem, I’m gonna fix this a few minutes later.
  4. PoseFeatureExtractor should be implemented before going into the learning experiments
  5. Gazebo range camera model doesn’t seem to acquire data from the top sides of the cylinders, this might be a problem if fill-able type affordances are to be learned.
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