Relational Learner

A ROS package that uses qualitative spatio-temporal relations to learn and classify human trajectories as statistically novel or not.

Prerequisites

  • roscore
  • mongodb_store
  • strands_perception_people
  • soma_trajectory
  • qsrlib

Getting started

  1. Run mongodb datacentres: $ roslaunch mongodb_store mongodb_store.launch db_path:= <path>     $ roslaunch mongodb_store mongodb_store.launch db_path:=/home/strands/mongodb_store/bham_trajectory_store/ where path specifies the path to your mongodb_store

Also run the mongodb robot pose logging tool: $ rosrun mongodb_log mongodb_log.py /robot_pose

  1. Make sure people perception is running to publish detected trajectories: $ roslaunch perception_people_launch people_tracker_robot.launch
  2. Also run the online-trajectory stiching tool: $ rosrun human_trajectory trajectory_publisher.py [publish_interval][online(1)/offline(0)] see here for more details.

Alternatively to step 2 and 3, you can run soma_trajectory and obtain test trajectories from mongodb store: $ rosrun soma_trajectory trajectory_query_service.py

  1. Make sure the QSRLib Service is running: $ rosrun qsr_lib qsrlib_ros_server.py

  2. Run the Episodes Service: $ rosrun relational_learner episode_server.py

  3. Run the Novelty Service:

    $ rosrun relational_learner novelty_server.py

  4. Run the Episodes node: $ rosrun relational_learner episodes_client.py

  5. Run Novelty node: $ rosrun relational_learner novelty_client.py

Note:

This package can be run offline by running soma_trajectory in step 2 and 3 instead of people_perception. In this case, step 7 becomes: $ rosrun relational_learner episodes_client_OT.py

which queries one region’s trajectories from mongodb_store instead of subscribing to published trajectories.

Original page: https://github.com/strands-project/trajectory_behaviours/blob/master/relational_learner/README.md