<p>Unstructured scene understanding in real-world environments remains difficult, such as how to understand unstructured paths in field environments using resource-constrained systems with extremely limited computational, memory, and energy resources. Traditional methods are too focused on the specific goal of detection or segmentation, and seriously neglect the sustainability of energy consumption, cost and complexity. In this study, we present a sustainable method to understand unstructured paths and reconstruct them in 3D space and plan walkable routes through a low-cost monocular camera. Edges lines are detected. Contour candidate curves are extracted by the relative integrity of lines. Based on geometric orientation continuity and coverage, candidates are refined to get approximate contours, which are regarded as walkable regions. By analyzing the relative geometric constraints between different walkable contours, unstructured paths can be understood and reconstructed in 3D space and walkable routes can be planned. This method requires no prior training, and no calibration of the internal parameters of monocular camera, which has the sustainability of low power consumption and low cost. We provide a comprehensive evaluation of the method in terms of multiple dimensions such as energy consumption, cost and interaction ratio. The results show that the method not only can understand unstructured paths, but also its sustainability of low power, low cost, and lightweight are more suitable for a resource-constrained system in field environments.</p>