This page contains notes on using visual place recognition on the PR2 for localization, solving the kidnapped / wake-up robot problem.

At a high level, there will be a node that continuously gathers data for performing place recognition as an already localized robot moves around in the world. It may actively engage with the navigation stack to stop and collect data when needed. This data is stored persistently so we have it on robot wake-up.

It will have a ROS interface (action?) to attempt to localize using place recognition when the robot is poorly localized (kidnapped or just woken up).

Gathering place data

Global pose estimates come from amcl:

  • Can listen on /tf for odom to map transform.

  • And/or listen to /amcl_pose and only collect data for pose estimates with low covariance. We want AMCL to be well-localized when gathering data.

Place database

At least for now, keep two separate databases. One in memory for doing the place recognition prefilter. Another containing other associated data (poses, keypoints, descriptors) for doing the geometric check and pose estimation. Both are keyed on the "document id" for that image (pair, for stereo).

An extension once basic system is working: replace old data when we revisit a place to give some robustness to changing environment.

Place recognition database:

  • Already implemented as vt::Database.

  • Need to flush to disk periodically.
    • Or consider storing each DocumentVector in appropriate row of SQLite DB to keep everything in one place.

  • For replacing old data: will need to add methods to remove / replace a document.

Associated data for pose estimation:

  • Shove into a simple SQLite database.
  • Indexed on document id.
  • Each row contains:
    • Time stamp.
    • Pose in map frame from AMCL.
    • Transform from camera frame to odom as head will be moving.

    • Keypoints and descriptors (binary BLOB).
  • Spatial queries
    • Can start with simple "sort on X, filter on Y" scheme.
    • Drop in SQLite R*Tree module if/when we need better scalability.

Getting sufficient coverage of building

When some distance (say >2m) from any place already in database:

  • Check AMCL well localized.
  • Halt base movement - need to interact with move_base action. Talk to Eitan about most sensible way to do that.

  • Rotate head to get 4-8 frames of 360 degree surroundings.
  • For each frame, compute keypoints & descriptors, store with other metadata in SQLite. Quantize descriptors and add document to vocabulary tree database.

  • Allow robot to continue on.

Can publish markers of where we took samples to rviz to see where we have coverage.

Would be nice to teleop robot around and have it just stop and collect data when appropriate. Maybe simplest to hack up pr2_teleop to allow place rec node to temporarily disable responding to the joystick. Skip 180 degrees backwards view (?) since someone will always be standing there.

Nicest would be for the robot to traverse the whole building autonomously to gather / refresh its data, but that can be implemented later.

Localizing by place recognition

When action / service invoked:

  • Halt base movement if necessary.
  • Rotate head to get 4-8 frames.
  • Do place recognition against known places in database, with geometric check.
  • If we get a good match, publish to AMCL's initialpose topic to (re-)initialize the particle filter.

Wiki: kidnapped_robot (last edited 2010-10-14 21:37:29 by KenConley)