Navigation aiding by a hybrid laser-camera motion estimator for micro aerial vehicles

Micro Air Vehicles (MAVs) equipped with various sensors are able to carry out
autonomous flights. However, the self-localization of autonomous agents is mostly dependent
on Global Navigation Satellite Systems (GNSS). In order to provide an accurate navigation solution
in absence of GNSS signals, this article presents a hybrid sensor. The hybrid sensor is a deep
integration of a monocular camera and a 2D laser rangefinder so that the motion of the MAV
is estimated. This realization is expected to be more flexible in terms of environments compared
to laser-scan-matching approaches. The estimated ego-motion is then integrated in the MAV’s
navigation system. However, first, the knowledge about the pose between both sensors is obtained
by proposing an improved calibration method. For both calibration and ego-motion estimation,
3D-to-2D correspondences are used and the Perspective-3-Point (P3P) problem is solved. Moreover,
the covariance estimation of the relative motion is presented. The experiments show very accurate
calibration and navigation results.