Augmented Visual SLAM for the Localisation of a Transportation Asset Management Survey Vehicle

In this paper we explore how visual simultaneous localisation and mapping (vSLAM) systems might estimate the pose (position and orientation) of a vehicle surveying highway assets, as part of a wider transport asset management (TAM) system. Such a system would reduce reliance on (or enhance estimation from) a GPS-enabled inertial measurement unit (IMU). Two problems with employing vSLAM systems on highway survey imagery are identified. Firstly, straight segments of the highway cause low parallax issues, and secondly the presence of other vehicles breaks the assumption that the camera moves within a static environment. To overcome these problems, an existing monocular vSLAM system is modified with two state-of-the-art deep neural network (DNN) tools. We show that the modified system provides an improved estimation of pose. In addition to TAM, our work has clear applications in autonomous vehicles, when there is only limited visual field sensing.