When considering the operation of an Unmanned Aerial Vehicle (UAV) or an Unmanned Ground Vehicle (UGV), such problems as landing site estimation or robot path planning become a concern. Deciding if an area of terrain has a level enough slope and a wide enough area to land a Vertical Take Off and Landing (VTOL) UAV or if an area of terrain is traversable by a ground robot is reliant on data gathered from sensors, such as cameras. 3-D models, which can be built from data extracted from digital cameras, can help facilitate decision making for such tasks by providing a virtual model of the surrounding environment the system is in. A stereo vision system utilizes two or more cameras, which capture images of a scene from two or more viewpoints, to create 3-D point clouds. A point cloud is a set of un-gridded 3-D points corresponding to a 2-D image, and is used to build gridded surface models. Designing a stereo system for distant terrain modeling requires an extended baseline, or distance between the two cameras, in order to obtain a reasonable depth resolution. As the width of the baseline increases, so does the flexibility of the system, causing the orientation of the cameras to deviate from their original state. A set of tools have been developed to generate 3-D point clouds from rigid and flexible stereo systems, along with a method for applying corrections to a flexible system to regain distance accuracy in a flexible system.