Dense 3D Reconstruction for Visual Tunnel Inspection using Unmanned Aerial Vehicle

TL;DR

This paper presents a UAV-based system with a single rotating camera that performs dense 3D reconstruction of tunnels, overcoming challenges of erratic camera motion and limited image overlap, to enable remote inspection of inaccessible tunnel environments.

Contribution

The work introduces a novel method combining Structure-from-Motion, Bundle Adjustment, and geometry priors for robust dense 3D reconstruction in tunnel scenarios with minimal images.

Findings

Successfully reconstructs dense 3D models of tunnels

Enables remote inspection of inaccessible environments

Works effectively with erratic camera motion and limited image overlap

Abstract

Advances in Unmanned Aerial Vehicle (UAV) opens venues for application such as tunnel inspection. Owing to its versatility to fly inside the tunnels, it can quickly identify defects and potential problems related to safety. However, long tunnels, especially with repetitive or uniform structures pose a significant problem for UAV navigation. Furthermore, post-processing visual data from the camera mounted on the UAV is required to generate useful information for the inspection task. In this work, we design a UAV with a single rotating camera to accomplish the task. Compared to other platforms, our solution can fit the stringent requirement for tunnel inspection, in terms of battery life, size and weight. While the current state-of-the-art can estimate camera pose and 3D geometry from a sequence of images, they assume large overlap, small rotational motion, and many distinct matching points between images. These assumptions severely limit their effectiveness in tunnel-like scenarios where the camera has erratic or large rotational motion, such as the one mounted on the UAV. This paper presents a novel solution which exploits Structure-from-Motion, Bundle Adjustment, and available geometry priors to robustly estimate camera pose and automatically reconstruct a fully-dense 3D scene using the least possible number of images in various challenging tunnel-like environments. We validate our system with both Virtual Reality application and experimentation with a real dataset. The results demonstrate that the proposed reconstruction along with texture mapping allows for remote navigation and inspection of tunnel-like environments, even those which are inaccessible for humans.