Local Bundling of Disparity Maps for Improved Dense 3D Visual Reconstruction

Abstract

This thesis presents a new method for improved resolution of stereoscopic 3D terrain mapping by local dense bundling of disparity maps. The Unmanned Systems Lab (USL) at Virginia Tech is designing an unmanned aerial vehicle (UAV) first-response system capable of 3D terrain mapping in the wake of a nuclear event. The UAV is a helicopter, and is equipped with a stereo boom imaging system, GPS, and an inertial measurement system (IMU) for low-altitude aerial mapping. Previous 3D reconstruction algorithms on the project used two-frame rectified stereo correspondence to create a local 3D map, which was geo-located by raw GPS and IMU data. The new local dense bundling algo-rithm combines multiple pairs of stereo images by SURF feature point matching, image rectification, matching of dense points with semi-global block matching, and optimization of camera pose and dense 3D point location using a stereo-constrained local bundle adjustment. The performance of the algorithm is evaluated numerically on synthetic im-agery and qualitatively on real aerial flight data. Results indicate the algorithm produces marked improvement in accuracy and vertical resolution, given a proper helicopter flight path and sufficient image overlap. With synthetic imagery and precise pose supplied, the algorithm shows a 1.2x to 6x reduction in vertical error.