Minutes:

• 2-page abstract due for this year. We'll develop it in the directory:
  • DOC/abstract inside SVN. Dave will start it and email around [DM]
• Qualification video - take it sometime that's convenient

• Start planning itinerary
  • 12 possibly interested {TH, AG, MM, CG, PF, MB, SH, DM, PV, SM, WW, TS}
  • SH to email the 4 supervisors, who will figure it out for us {JL, DL, BW, AM}
  • For budgeting reasons, the costs are roughly:
    • >= $380 US round-trip for flights
    • ~ $55/night per room at the Monte Carlo (cheaper elsewhere)
    • ~ $2000 for robot shipping
    • Potentially some conference registrations ($400.00 for students)

• Catherine update on shipping
  • Cost for shipping is about the same as cost to rent a car and drive
  • MM might be ok with driving his own car. Wonder how much UBC/SRVC would compensate him for putting 6K on his new Jetta
  • Debate on driving vs shipping on several axes:
    • Shipping:
      • Pros: Doesnt take our time, if we invest in a crate (~$800), we always have it later
      • Cons: Potential for damage, extra downtime for robot with packaging
    • Driving:
      • Pros: We keep the robot in our possession, saves cost on flights
      • Cons: Takes a lot of our time
  • Major issue seems to be how long the shipping downtime would be. CG to check out.
  • Also would like to know if it's possible to rent a truck with unlimited km. SH to check out.
  • Dont forget that loading the robot into anything without a ramp is difficult.
  • Need to decide soon.
• Theakston and Lapinkulta are self-admined and can be used for development. Log in with lciuser... and sudo adduser to make your own account.
• Finally, the list of completed and in progress tasks.

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

Current in-progress task list:

• Capture data from robot for testing
• Basic robot functions based on ROS. (with aim to perform a preliminary test run of navigation and mapping) [MM and DM]
  • WG nav stack
  • Tower upgrade:
    • Order material for building a new laser/camera mount and assemble same.
  • Saliency maps and visual attention
    • Basic saliency map computation [DM]
    • Stereo + saliency combined to identify interesting regions [PV]
• 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
  • Choice of "where-to-look" aka attention system
• Recognition framework (James module directly or something built upon that) [AG and CG]
  • Combining results from different types of detectors (different algorithms)
  • Combining results from various viewpoints * We'll meet on the previous two topics tomorrow
  • Collect data for 5 "given" object classes once they're published
  • 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
• 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]
  • Initial investigation to verify this is a doable task (profiling current code, ensuring good performance on web data, investigation of potential speedups such as GPU feature extraction and SVM learning)

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.