Long Range Tag Detection

Context: URC rules dictate that we must detect ArUco tagsthat are placed upon posts:

The 3 posts will have GNSS coordinates that are within the vicinity of the posts, increasing in range from approximately 5-20 m.

Please also read the Wiki page on perception.

Problem: The ZED stereo camera can only detect tags around 8 meters away. As such we have to run a spiral search to find the tag. This takes precious time since the autonomy period is only 30 minutes long.

Solution: Longer distance tag detection via a secondary camera (compared to just the ZED):

• Process at higher resolution and lower frame rate
• Use optical magnification via a higher focal length lens
• Detect direction of tag much earlier
• Does not have to be stereo
• Navigation will run a control loop to center the tag on the screen, and then drive forward
• Navigation will obey 3D readings from ZED when close enough

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Interface (subject to change)

Node: long_range_tag_detector

Subscribes: sensor_msgs/Image

Publishes:

Tags.msg

Tag[] tags

Tag.msg

uint8 id
float32 bearing

Note: This will most likely have to run as a nodelet under the perception_nodelet_manager since passing Image messages is costly otherwise.

Rough Steps:

1. Create a subscriber for the Image topic
2. Write a function that takes an Image and detects tags
3. Calculate the bearing of the tag based on the camera FOV (see below)
4. Create a publisher for the Tag topic
5. Write a function that publishes the detected tags from the Image message to the Tag topic

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Tag Bearing To tell navigation where to drive, we must compute a bearing from the tag information. This can be done using information about the ZED's camera intrinsics and the tag's location in pixel space.

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We also need a Node that can deliver an Image message. Try out this premade usb_cam package first. You should be able to install it via sudo apt install ros-noetic-usb-cam. If it is too slow we can try rolling our own using V4L or cv::VideoCapture.

Rough Steps:

1. Try the usb_cam package first
2. If not, summarize your findings to a lead. We are targeting around 15-30 Hz update rate.
3. The lead will create a C++ node if we are not satisfied
4. Try to use cv::VideoCapture to read a cv::Mat and publish that as an Image message
5. If too slow, try V4L directly
6. We will go from there