Monocular Curb Edge Detection via Robust Geometric Correspondences

Advanced driver-assistance and autonomous systems require perception that is both robust and affordable. Monocular cameras are promising due to their ubiquity and low cost, yet detecting abrupt road-surface irregularities such as curbs and bumps remains challenging. These sudden road gradient changes are often only a few centimeters high, making them difficult to detect and resolve from a single moving camera. We hypothesize that stable image-based homography, derived from robust geometric correspondences, is a viable method for predicting sudden road-surface gradient changes. To this end, we propose a monocular, geometry-driven pipeline that combines transformer-based feature matching, homography decomposition, temporal filtering, and late-stage IMU fusion. In addition, we introduce a dedicated dataset with synchronized camera and ground-truth measurements for reproducible evaluation under diverse urban conditions. Our results show that homography-based cues can capture subtle plane changes reliably, supporting the feasibility of camera-centric solutions for safety-critical perception tasks. The code and the collected data will be made publicly available at: https://norbertmarko.github.io/curb_detection/