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Input processing

✎

Modified 2019-10-06 by AmaurX

The pipeline to treat the raw input to the system and transform it for the graph.

Knowing the type of input to the system

Knowing how this input is processed for the localization graph

Section 2.1 - Motivation
Section 2.2 - Apriltag detection strategies
Section 2.3 - Apriltag extraction
Section 2.4 - Odometry processing (wheel odometry)

Motivation

✎

Modified 2019-10-06 by AmaurX

From Unit F-1 - Input to the system, we now know that we have many streams of image input, from both Watchtowers and Autobots. These images contain various Apriltags, that are placed and registered in the city as described in Unit E-2 - BUILDING - Apriltags specifications.

These Apriltags need to be processed in order to feed the associated transforms to the graph builder. This means that on every image, for every Apriltag on the image, a transform is computed that links the camera_frame to the apriltag_frame, keeping in memory the name of the agent (Watchtower or Autobot) that detected the Apriltag, as well as the Apriltag unique ID, and the timestamp at which the image was taken. This package of information makes up what will be called everywhere stamped transform.

We also have the wheel commands streams of each Autobots. These need to be processed into stamped transform as well, as those are the only thing we want to feed the localization graph with.

Notation: This parts receives images and wheel commands and outputs stamped transforms to the ROS Listener.

Apriltag detection strategies

✎

Modified 2020-07-18 by Andrea Censi

Apriltag detection is very computationally expensive, and different strategies can be used:

Offline acquisition : each agent just records the images, then they are gathered and processed later, offline, without any time requirement. This is the easy, but unsatisfactory way. This also means that the localization can only be used a posteriori, so it can not be used for real time decision making.
Online acquisition : the images are processed during the experiment, and the graph and localization is done (with some delay) online. This is much harder to do as processing images is costly.

For online acquisition, three strategies can be used:

Each agent tries to do the processing directly onboard and just sends the stamped transforms to a central ROS master that will do the graph
Each agent directly sends the images and the central ROS master does the processing for every agent (this implies having a (or many) good computer)
A mix of the two is also possible (the Autobots send their images, the Watchtowers process them)

As explained in Unit E-3 - DEMO - Localization, in both cases the Watchtowers and Autobots use the acquisition bridge to send their image streams to a central computer. The Watchtowers only send images when movements are detected, to reduce the number of images to process and record.

For offline acquisition, all we need to do is record a rosbag on this computer.

For online acquisition, the stream of data needs to be used directly by an apriltag extractor.

Apriltag extraction

✎

Modified 2019-10-06 by AmaurX

No matter how (or on which device) we get the image streams, we need to process them to get the stamped transforms of each Apriltag in each image.

Offline case

✎

Modified 2019-10-06 by AmaurX

For the offline case, where speed is not relevant, we get a rosbag from the recording. We feed this bag to a post processor, which code is in part 08 of the cslam repository.

This code will run apriltag extraction as well as odometry processing and it will export all the corresponding stamped transforms to a new bag.

Online case

✎

Modified 2019-10-06 by AmaurX

For the online case, we cannot just use one container to do all the extraction. The current strategy is to instantiate one apriltag processor per Watchtower. Each processor is one container that only listens to the image topic of the Watchtower it was assigned to, and outputs the processed stamped transforms. The code is in part 04 of the cslam repository. It is mainly exactly the same process as int the post processing container.

Todo: explain AT detection process and code, etc…

task (6 of 17) index

task

The following was marked as "todo".

Todo: explain AT detection process and code, etc…

Location not known more precisely.

Created by function n/a in module n/a.

Odometry processing (wheel odometry)

✎

Modified 2019-10-06 by AmaurX

In the wheel odometry case, we can listen to the wheel command topic or get it from a rosbag (same process for offline/online as for the Apriltag detection).

But no matter the way we get the data, here is how we process it:

An odometry message is received with timestamp t1.
The next one is received with timestamp t2 > t1 .
The wheel velocities are considered constant between t1 and t2, with values from the odometry message at time t1.
From the differential model of the Autobot, linear velocity V_l and angular velocity Omega can be computed
From those constant velocities, the transform between t1 and t2 can be computed
The transform is stamped with t1 and with the Autobot’s ID, which makes it a stamped transform, that is then sent to the localization graph.

Future work

✎

Modified 2019-10-06 by AmaurX

Right now, the acquisition bridge for Autobots sends the wheel commands, but future work could imply using more advanced modeling and system identification of the Autobots to output more accurate linear velocity V_l and angular velocity Omega. Then the above algorithm would just skip the phase where it transform the wheel commands into V_l and Omega.

Todo: Actually give the equations

task (7 of 17) index

task

The following was marked as "todo".

Todo: Actually give the equations

Location not known more precisely.

Created by function n/a in module n/a.