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Input to the system

Modified 2019-10-06 by AmaurX

The input data fed to the localization system

Images from the Autobots

Modified 2019-10-06 by AmaurX

The Autobots already use a camera feed that is medium quality on the normal pipeline (640x480). All we do here is use the same image stream.

Images from the Watchtowers

Modified 2019-10-06 by AmaurX

For the Watchtowers it is a different story. As it is not used (yet) for anything else, we decided for the below reason to use a better image resolution (1296x972) and a higher shutter speed:

  • The cameras are high, so the relative size of the Apriltags is small, so higher resolution mean better detection of the Apriltags (especially on the edges of the image)
  • The Autobots are (mostly) always moving, and this creates blur with the default shutter speed. With a higher shutter speed, the blur is decreased, and the resulting loss of light is barely noticeable.

Todo: add images to see the difference

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Todo: add images to see the difference

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Odometry data from the Autobots

Modified 2019-10-06 by AmaurX

Here there are (at least) two options, but the result must be the same: What is needed is an odometry stream input.

An odometry input is a sequence of relative transform from position at time t to position at time t+dt of the Autobot, with dt being the sampling time.

The currently available options are :

  • Wheel command odometry : From the command input of the wheel we construct the odometry
  • Visual odometry : From the image stream of the duckiebot we construct odometry

Let’s discuss the pros and cons of each method.

The wheel command odometry

Modified 2019-10-06 by AmaurX

Note : Right now, this is the used method

The pros:

  • Very easy and fast to compute
  • Can be done in real time
  • The data is small to store and to transmit over network
  • Is compliant with the dynamics model (no Y or Z velocity)
  • Can use any identified model of the Autobot (gain-trim, kinematic, dynamic)

The cons:

  • It is an input based odometry : very small worth
  • Doesn’t account for slipping (if the autobot is stuck but the wheels spin, the odometry continues to believe in forward movement)
  • Has only been used with the simple and inaccurate gain-trim model

The visual odometry

Modified 2019-10-06 by AmaurX

Note : Right now, this is not used, but it would be preferable in the long term

The pros:

  • Relies on actual output data
  • Will easily account for slipping, as the image won’t change
  • Many libraries exist that can be used

The cons:

  • Very slow to process
  • Impossible to run real time on an Autobot
  • Does not take dynamic model into account, which can generate noise on Y and Z velocities (but to be fair this could be then be ignored)
  • No library has actually been extensively tested on Autobots.