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This paper presents a visual-inertial odometry-enhanced geometrically stableIterative Closest Point (ICP) algorithm for accurate mapping using aerialrobots. The proposed method employs a visual-inertial odometry framework inorder to provide robust priors to the ICP step and calculate the overlap amongpoint clouds derived from an onboard time-of-flight depth sensor. Within theoverlapping parts of the point clouds, the method samples points such that thedistribution of normals among them is as large as possible. As differentgeometries and sensor trajectories will influence the performance of thealignment process, evaluation of the expected geometric stability of the ICPstep is conducted. It is only when this test is successful that the matching,outlier rejection, and minimization of the error metric ICP steps are conductedand the new relative translation and rotational components are estimated,otherwise the system relies on the visual-inertial odometry transformationestimates. The proposed strategy was evaluated within handheld, automated andfully autonomous exploration and mapping missions using a small aerial robotand was shown to provide robust results of superior quality at an affordableincrease of the computational load.

Visual-Inertial Odometry-enhanced Geometrically Stable ICP for Mapping Applications using Aerial Robots