Predicting road surface normal and pitch with image-based algorithms remains a significant challenge, especially when steep inclines, declines, and sudden changes in road inclination are involved.
To solve this problem, we propose a novel image-based algorithm that leverages homography decomposition to achieve accurate ground plane normal and pitch estimation.By integrating a Kalman Filter, our method en- hances prediction stability. Further, we incorporate robust sensor fu- sion by integrating IMU-based odometry, ensuring that the estimates are accurately aligned with real-world motion. Overall, our approach outperforms existing techniques in both accuracy and responsiveness for dynamic driving environments.
Experimental results show that our approach achieves superior performance, reducing average pitch and normal errors by 0.493° and 0.483°, respectively, compared to the current state-of-the-art, and exhibits a shorter transient response in case of a sudden road inclination change. Code will be available at https://norbertmarko.github.io/ground-normal-prediction/
@article{marko2025roboust,
author = {Norbert Markó and Zoltán Rózsa and Áron Ballagi and Tamás Szirányi},
title = {Robust Road Surface Normal and Pitch Estimation via IMU-Camera Fusion},
journal = {},
year = {2025},
}