How do Robots Know Where they Are? (Fri Oct 11, lecture 12) previous next
Using landmarks to determine location

How Do Robots Know Where They Are?
  • Big idea: Robots estimate their position and orientation in the world using a combination of odometry, visual land- marks, and other types of sensory information.
  • Underlying Technologies
    • Particle filters
    • SLAM
  • Learning Goals: Students will:
    • Understand the uses and limitations of odometry and visual landmarks
    • Understand the basic principles of particle filters, and how particle filters are used for localization.
    • Demonstrate a robot avoiding obstacles using AMCL
    • Demonstrate effective robot navigation behavior by arranging landmarks appropriately in the environment and invoking the Pilot’s localization mechanism as needed to determine their robot’s position.

Homework due for today

Legend: : Participation (pass/fail) | : PDF | : Team | : Attachment

  1. Do Homework: Double Follow with tf2. Follow the instructions and submit the requested deliverables.

Your feedback t me

Hard because of Time Constraints
  • More papers to read
  • More demonstrations of “new” things, e.g. sensors, arm, etc
  • Show how to use other packages, e.g. pid
Will try to do
  • Let us form teams ourselves
  • More code walkthrough and explanations of code
  • Review code after submission so that we can learn and improve (2x)
  • More troubleshooting instructions
  • More relaxed in lecture
  • More diverse students
  • Strategies to solve problems in addition to examples.
Need discussion
  • Clearer grading and rubrics - whats the difference between a 97 and 100? (2x)
  • More robot specific examples of actions and services early on
  • Less frequent, and harder assignments, slower pace for larger deliverables (2x)
  • More guided labs (there seem to be people falling behind)
  • More experimental, less theoretical
  • In class tutorials of all mathematical concepts

Lab today

  • What (if any) guided lab topic?

Localization

What exactly is the problem with knowing where you are?
  • Remember all of this needs an agreed upon coordinate system!
  • How would you do it with your eyes closed?
  • Recall odometry is like dead reckoning
  • Lots of noise in the signal; accuracy varies; errors build up
What kinds of landmarks can be used?
  • Lidar detected fixed obstacles
  • Vision detected fixed obstacles
  • How can one obstacle be distinguished from another?
Lidar detection
  • Robot has a ‘map’ of fixed obstacles
  • Robot compares that map with the apparent, transient, map from Lidar
  • Calculates a probability distribution of where it might be on that map
  • Process is called AMCL
Visual Detection
  • Robot has a collection of scenes it can recognize.
  • Some requirements:
    • CV (computer vision) algorithms need to be able to identify and differentiate it from other images
    • A coordinate in 3D space is required
    • It needs to stay put and not move
    • Examples: facade of a building, a particular tree, a wall, etc.
    • Bad examples: a parked car; a person
  • With each scene is the coordinates that correspond to the recognized scene
  • CV is constantly analyzing what is seen by the camera
  • As soon as it identifies an image, it can use that to figure out where it is
  • Note! It has to also figure out where IT is relative to the image
AMCL
  • Adaptive Monte Carlo Localization
  • Very sophisticated (and standard) mathematical technique
  • Can be used with different kinds of sensors.
  • Example here is LIDAR.
    • Requires a map of Lidar visible obstacles, with a coordinate system, and anchored in the real world
    • Given a stationary robot, and a lidar scan, what does it see?
    • Look for a match on the map
    • Form a probability distribution of where the robot MIGHT be
    • Move the robot a little.
    • Compute what the lidar would see given what is known about the motion
    • Update the probabilities
SLAM
  • What if there is no map yet?
  • Simultaneous localization and mapping
  • Create a theoretical map based on view of the LIDAR
  • Move the robot a little and update the map
  • Travel through the relevant region (using e.g. Teleop)
  • Use the map to localize, or where there is no map yet, extend the map.

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