Visual Simultaneous Localization and Mapping with Applications to Monitoring of Underground Transportation Infrastructure

Abstract

This paper presents the results of the development, design, and implementation of a visual simultaneous localization and mapping (SLAM) system for autonomous real-time localization with application to underground transportation infrastructure (UTI) such as tunnels. Localization is achieved in the absence of any global positioning system (GPS) or auxiliary system. The indoor localization system is a necessary element of a fully autonomous platform for the detection of cracks and other anomalies on the interior surfaces of tunnels and other UTI. It can be used for tagging of high-resolution sensor data obtained with low-cost prototype data acquisition platforms previously developed. Visual based SLAM has been used as the core element in an architecture employing a commercial off-the-shelf (COTS) ZED stereo camera from Stereolabs. To achieve real-time operation, an NVIDIA Jetson TX2 massively parallel graphics processing unit (GPU) was used as the core computational engine employing two different software libraries. We achieved localization at 5 frames per second (FPS) using ORBSLAM2 open-source software library, and the much lighter, but proprietary, ZED SDK was able to deliver a performance at nearly 60 FPS. To assess the accuracy of the relative localization system, we conducted several tests at 30 FPS and reported on the resulting error variances that were found to be consistently very small. Finally, we conducted several tests in a tunnel in the Los Angeles county area and confirmed the applicability of the method for monitoring UTI.