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Version: 13.1

Generating a Calibration using MetriCal

Required Prerequisites

This tutorial assumes you've gone through all of the prerequisite setup steps in the intro like getting a license and installing MetriCal.

You will also need an object space file and a data capture to process. If you are following along with the tutorial series, you will have procured these in Part 1 and Part 2 respectively.

1: Initialize MetriCal​

A plex is a virtual snapshot of your system's calibration at a point in time. Init creates a raw input plex by inferring it from the data it is given. We'll optimize this plex, and this data, using Calibrate later.

metrical init \
  --topic-to-model *cam*:opencv_radtan \ # Every topic with "camera" string gets the RadTan model
  --topic-to-model *lidar*:no_offset   \ # Every topic with "lidar" string gets no model
  --topic-to-model *imu*:scale_shear   \ # Every topic with "imu" string gets Scale-Shear model
  $DATA                                \ # Provide our data
  $INIT_PLEX                             # Our output: a fresh plex!

More on...​

2: Calibrate the Initial Plex​

The actual calibration! This refines the plex, giving you accurate calibation values. We'll visualize detections by passing the --render flag. Make sure you have a Rerun instance running!

metrical calibrate            \
  --render                    \ # Render the calibration process using Rerun
  --output-json $OUTPUT       \ # Write the results to $OUTPUT
  $DATA                       \ # Our input calibration dataset
  $INIT_PLEX                  \ # Our fresh plex from Step 1
  $OBJ                        \ # Our object space file describing fiducials and targets

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3: Render the Results​

Display mode shows us what the calibration looks like when applied to a dataset. Usually, you'll just apply it to the same dataset you used for calibration, but you can pass in any dataset that uses the same topic names. Make sure you have a Rerun instance running!

metrical display $DATA $OUTPUT

4: Shape the Plex Output​

This shapes the calibration output into something more useful for our system. In this case, we want to create a stereo rectification for our system.

metrical shape       \
  $OUTPUT            \ # Use the calibration output we derived from Step 2
  $OUTPUT_DIR        \ # Write the results to $OUTPUT_DIR
  stereo-lut         \ # Create a pair of stereo rectification lookup tables
  --dominant ir_one  \ # The dominant eye in this stereo pair
  --secondary ir_two \ # The secondary eye in this stereo pair

More on...​