  Perception is one of the key abilities of autonomous mobile robotic systems,
which often relies on fusion of heterogeneous sensors. Although this
heterogeneity presents a challenge for sensor calibration, it is also the main
prospect for reliability and robustness of autonomous systems. In this paper,
we propose a method for multisensor calibration based on Gaussian processes
(GPs) estimated moving object trajectories, resulting with temporal and
extrinsic parameters. The appealing properties of the proposed temporal
calibration method are: coordinate frame invariance, thus avoiding prior
extrinsic calibration, theoretically grounded batch state estimation and
interpolation using GPs, computational efficiency with O(n) complexity,
leveraging data already available in autonomous robot platforms, and the end
result enabling 3D point-to-point extrinsic multisensor calibration. The
proposed method is validated both in simulations and real-world experiments.
For real-world experiment we evaluated the method on two multisensor systems:
an externally triggered stereo camera, thus having temporal ground truth
readily available, and a heterogeneous combination of a camera and motion
capture system. The results show that the estimated time delays are accurate up
to a fraction of the fastest sensor sampling time.
