logistics

• New Lab Schedule
• Some labs have important activities; not all, but some
• Mandatory for all students
• Thursday’s from 3:55pm to 5:55pm
• If you have a serious conflict, you can leave a little earlier
• But you need to be there and you need to talk to me before leaving
• Thursday morning 10-12 is still a lab with TA and/or Pito staffing

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?
• Color, Size, Shape
• Note that in addition to recognizing, we need to know the distance

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 - Adaptive Monte Carlo Localization

Visual Detection

• Robot has a collection of scenes it can recognize.
• Needs to meet certain characteristics for it to work
• 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

Requirements Visual localization

• 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

What is needed?

• Goal: somehow we need to determine the TF between tf(Odom) and tf(Map)
• Can you see how that single transform will do it?

AMCL - Important algorithm for localization

• Adaptive Monte Carlo Localization
• Very sophisticated (and standard) mathematical technique
• Can be used with different kinds of sensors.

How AMCL works with Lidar

1. Requires a map of Lidar visible obstacles, with a coordinate system, and anchored in the real world
2. Given a stationary robot, and a lidar scan, what does it see?
3. Look for a match on the map
4. Form a probability distribution of where the robot MIGHT be
5. Move the robot a little.
6. Compute what the lidar would see given what is known about the motion
7. 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.

Thank you. Questions?  (random Image from picsum.photos)