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

TL;DR

This paper introduces a visual-inertial odometry-enhanced ICP algorithm for aerial robot mapping, improving robustness and accuracy by integrating odometry priors and geometric stability evaluation.

Contribution

It presents a novel method combining visual-inertial odometry with a geometrically stable ICP approach for improved aerial mapping accuracy.

Findings

Robust mapping results in autonomous aerial exploration.

Enhanced point cloud alignment with geometric stability checks.

Achieved superior mapping quality with manageable computational costs.

Abstract

This paper presents a visual-inertial odometry-enhanced geometrically stable Iterative Closest Point (ICP) algorithm for accurate mapping using aerial robots. The proposed method employs a visual-inertial odometry framework in order to provide robust priors to the ICP step and calculate the overlap among point clouds derived from an onboard time-of-flight depth sensor. Within the overlapping parts of the point clouds, the method samples points such that the distribution of normals among them is as large as possible. As different geometries and sensor trajectories will influence the performance of the alignment process, evaluation of the expected geometric stability of the ICP step is conducted. It is only when this test is successful that the matching, outlier rejection, and minimization of the error metric ICP steps are conducted and the new relative translation and rotational components are estimated, otherwise the system relies on the visual-inertial odometry transformation estimates. The proposed strategy was evaluated within handheld, automated and fully autonomous exploration and mapping missions using a small aerial robot and was shown to provide robust results of superior quality at an affordable increase of the computational load.