Skip to main content
Version: dev-latest

MetriCal Command: Plex Learn

Usage​

Plex Learn - CLI Example
metrical plex learn [OPTIONS] [PLEX_OR_RESULTS_PATH]
Plex Learn - Manifest Example
command = "plex-learn"
input-plex = "input.json"
dataset = "{{variables.dataset}}"
topic-to-model = [
  ["topic", "model"],
  ["topic2", "model2"],
]
output-plex = "{{auto}}"

Purpose​

metrical plex learn infers Plex information — components, intrinsics estimates, covariances, and constraints — from a dataset and/or a system specification. The result is a Plex ready to be passed to metrical calibrate.

This command replaces the now-deprecated metrical init for the day-to-day workflow of initializing a Plex from a dataset.

At least one of --dataset or --system-specification must be provided.

  • Use --dataset to infer components and constraints directly from raw observations in a dataset (MCAP or folder-based).
  • Use --system-specification to infer spatial constraints from a system specification.
  • Both can be provided together.

An optional input Plex can be supplied as a positional argument. Any components or constraints already present in that Plex will be carried through and enriched by the learning process.

Examples​

Learn a Plex from a dataset​

Assign all topics matching *cam_ir* to the OpenCV Radtan model and *lidar* to the LiDAR model:

metrical plex learn \
    --dataset $DATA \
    -m '*cam_ir*:opencv-radtan' \
    -m '*lidar*:lidar' \
    -o $PLEX

Learn from a system specification only​

metrical plex learn --system-specification system.toml -o $PLEX

Augment an existing Plex with dataset information​

metrical plex learn --dataset $DATA -m /camera/1:opencv-radtan input_plex.json -o $PLEX

Learn from a merged set of plexes​

metrical merge plex_cams.json plex_lidar.json -K complement \
    | metrical plex learn --dataset data.mcap

If plex learn is given an input Plex that already contains components and constraints, the learning process will enrich that Plex rather than starting from scratch. This lets you merge disparate calibrations across one system into a single Plex and then run plex learn to fill in any missing information.

Arguments​

[PLEX_OR_RESULTS_PATH]​

An optional path to an existing input Plex. If provided, its components and constraints will be carried through and enriched by the learning process. If omitted, MetriCal will attempt to read a Plex from stdin; if nothing is available on stdin, a new empty Plex is used as the starting point.

Options​

Global Arguments​

As with every command, all global arguments are supported (though not all may be used).

-D, --dataset [DATASET]​

The dataset to learn/infer components and constraints from. Accepts an MCAP file (.mcap extension) or a top-level directory containing a set of nested directories for each topic.

Expected to contain raw observations (e.g., images, point clouds, IMU samples). Cached detections or results from a prior MetriCal run are not supported here.

-S, --system-specification [SYSTEM_SPECIFICATION]​

A system specification file to infer spatial constraints from.

-m, --topic-to-model [TOPIC_NAME:MODEL]​

A mapping of topic/folder names to intrinsics models. All topics intended for calibration must be enumerated by this argument. Any matching topic models already in the input Plex will be overwritten.

Example:

-m /camera/1:opencv-radtan -m /camera/2:opencv-fisheye

Wildcards are also supported:

-m '/camera/*:opencv-radtan'

Available models​

Cameras​
LiDAR​
IMU​
Local Navigation Systems​

-o, --output [OUTPUT]​

The path to output the resulting Plex. If omitted, MetriCal will write the Plex to stdout.