Depth Sensor Calibration by Tracking an Extended Object

In this paper, we propose a novel algorithm for automatically
calibrating a network of depth sensors, based on a moving calibration object.
The sensors may have non-overlapping fields of view in order to avoid interference.
Two major challenges are discussed. First, depending on where the object is
located relative to the sensor, the number and quality of the measurements strongly varies.
Second, a single depth sensor observes the calibration object only from one side.
Dealing with these challenges requires a simple calibration object as
well as an algorithm that can deal with under-determined measurements of
varying quality. A recursive Bayesian estimator is developed that determines the
extrinsic parameters by measuring the surface of a moving cube with
known pose. Our approach does not restrict the configuration of the network and
requires no manual initialization or interaction.
Ambiguities that are induced by the rotational cube symmetries are
resolved by applying a multiple model approach. Besides synthetic evaluation we
perform real data experiments and compare to state-of-the-art calibration.