Calibrations Overview
Scenes including both a 2D and 3D representation such as lidars_and_cameras deal with data in a number of different
coordinate systems. These scenes require a calibration that relate the different sensors to
each other in terms of location and orientation. The calibration should also contain the required information for
projecting 3D points into the image plane of the camera.
A Calibration object consists of a set of key-value pairs where the key is the name of the sensor (i.e. sensor name)
and the value is either a LidarCalibration object or any of the different camera calibrations.
Note that after a calibration has been created you can, and should, reuse the same calibration for multiple scenes if possible.
Types of calibrations
All calibration types contain information about the position and orientation of the sensor in 3D. This is relative to the reference coordinate system. More information about lidar calibrations can be found here. The calibration also contains information about mapping 3D points to the image plane of the camera. For cameras, we support different types of standard camera calibrations, where you only have to provide the intrinsic parameters of the camera. If your camera model is not supported, you can also provide a custom camera calibration where you provide the implementation in the form of a WebAssembly module.
Examples
Creating a calibration
The following example code shows how you can create a unity (i.e. we assume that all sensors are placed at origin and have no rotation) calibration for a LIDAR sensor and several camera sensors of different types.
loading...
Listing existing calibrations
As a final step we can fetch the calibration via the external id. This can either be done via the client, or via the CLI kognicutil tool.
client.calibration.get_calibration(external_id="Collection 2020-06-16")
$ kognicutil calibration --external-id "Collection 2020-06-16"