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Helping mobile robots understand curved corridor scenes has considerable value in computer vision. However, due to the diversity of curved corridor scenes, such as curved structures that do not satisfy Manhattan assumption, understanding them remains a challenge. Curved non-Manhattan structures can be seen as compositions of spatial right angles projected into two dimensional projections, which may help us estimate their original posture in 3D scenes. In this paper, we presented an approach for mobile robots to understand curved corridor scenes including Manhattan and curved non-Manhattan structures, from a single image. Angle projections can be assigned to different clusters via geometric inference. Then coplanar structures can be estimated. Fold structures consisting of coplanar structures can be estimated, and curved non-Manhattan structures can be approximately represented by fold structures. Based on understanding curved non-Manhattan structures, the method is practical and efficient for a navigating mobile robot in curved corridor scenes. The algorithm requires no prior training or knowledge of the camera’s internal parameters. With geometric features from a monocular camera, the method is robust to calibration errors and image noise. We compared the estimated curved layout against the ground truth and measured the percentage of pixels that were incorrectly classified. The experimental results showed that the algorithm can successfully understand curved corridor scenes including both Manhattan and curved non-Manhattan structures, meeting the requirements of robot navigation in a curved corridor environment. [ABSTRACT FROM AUTHOR]