Oct142009 < LCI

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---++ Agenda

   * Start planning itinerary update from Scott
   * Robot shipping plan
   * Self-administered PC and networking update
   * List of pre-known objects posted
   * Demo of working image processing nodes
   * Finally, the list of completed and in progress tasks.

---++ Minutes

   * Everyone should contribute to the abstract. 
      * It can be checked out like this: svn co svn+ssh://username@cascade.cs.ubc.ca/lci/project/raid1/srvc/SVN/DOC/abstract
   * Bug Scott about planning who's going to Vegas
   * Start looking into renting SUV/minivan to drive the bot to vegas. In parallel, Catherine will investigate shipping options in case those look better.
   * Robot network setup is complete. From any locally administered machine, we should be able to drive the robot
      * Put this in your .bashrc.ros file:

function fraser() {

export ROS_MASTER_URI=http://fraser:11311
}

      * Then type "fraser()" in your shell in order to have ROS use the robot's roscore
      * Test this by typing "rostopic list" and make sure you see the robot's devices

   * This is the list of pre-known objects, grouped by our assessments:
      * We can probably do these already:
         * laptop
         * toy car
         * toy Stegosaurus
         * table lamp
      * These objects are really going to need some contours or structure:
         * mug
         * bottle
         * bowl
         * Frying pan
      * Not sure if there's any point working on:
         * pencil
         * toy bed
      * We need to obtain a bunch of these objects for testing. We'll go buy more if needed. [ALL]

    

---++ Completed components:

   * Porting of basic drivers for: bumblebee, cannon and powerbot
   * Tower design
   * gmapping
   * Tilting laser drivers
   * Robot coordinate transform code
   * Network configuration and development environment
      * Robot router setup
      * Setup self-administered PC's
      * ROS instructions
   * WG nav stack
   * Basic saliency map computation
  
   
---++Current in-progress task list:

   * Capture data from robot for testing
   * Tower upgrade:
      * Order material for building a new laser/camera mount and assemble same.
   * Attention system:
      * Stereo + saliency combined to identify interesting regions [PV]
      * Tilt laser point cloud segmentation [MM]
      * Choice of where to look
   * High-level control functionality such as planning
      * Random walk behavior
      * 3 main high-level planners:
         * Exploring frontiers
         * Find tables [PV]
         * Space coverage
         * Look back at objects
      * Top level state machine to choose between above planners
   * Recognition framework (James module directly or something built upon that) [AG and CG]
      * Skeleton framework for the recognition system (inputs: robot images, outputs class guesses)
      * Combining results from different types of detectors (different algorithms)
      * Combining results from various viewpoints
      * Evaluate on the known objects
      * Test data interface
      * Felzenswalb detector
         * MB profiled Kenji's python implementation - most of the time in convolution - promising
         * Will investigate cuda'ing pieces
      * Helmer detector
         * Using point cloud, 
      * Mccann
      * Training data interface and additional parameters 
      * Load balancing between various recognition algorithms
   * Cuda on fraser [MB, WW and TH]
      * Need to get the code compiling
      * GPUSift
      * FastHOG
   * Web grabbing module [PF and CG]
      * Add additional sources of info
      * Investigate filtering techniques
      * Integrate output data format with classification
   * Speed-up of Felzenswalb training [MB]
      * Data transfer kills several ideas we've had about converting to Cuda
      * Kenji suggested several non-GPU speedups which Matt will work on next
      * 
      
---++ Future tasks pending completion of others:

   * Use of 3D models in recognition
   * Use of 3D information and context in attention system
   * Real time result reporting
   * Feeding back classification results to robot planner
   * Investigate new cameras which might be faster than the Cannon
   * Prioritizing computation done by classifiers towards images which look really promising to the attention system, and based on the classes which have already been recognized.

-- Main.DavidMeger - 14 Oct 2009
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Topic revision: r3 - 2009-10-28 - DavidMeger