Applied Vision
Authors: Christian Schweighofer (University of Applied Sciences Upper Austria) , Michael Zauner (University of Applied Sciences Upper Austria)
Autonomous mobile robots must navigate dynamic environments safely, yet high end depth sensors are often expensive or impractical. Monocular cameras are widely available, but estimating metric depth and detecting obstacles in real time remain challenging. We address this by implementing a pipeline that combines monocular depth estimation with metric scale calibration, 3D back-projection, filtering, and clustering. Our marker-based calibration achieves a depth RMSE as low as 13mm, while the proposed pipeline successfully detects all 8 obstacles in our evaluation. With OpenVINO optimizations, the model achieves an inference rate of up to 17FPS, establishing a foundation for real-time processing. Overall, the pipeline demonstrates promising results for safe navigation using only monocular cameras on resource-constrained robots, evaluated in the context of the international robotic contest Eurobot.
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How to Cite: Schweighofer, C. & Zauner, M. (2026) “Obstacle Detection Pipeline using Monocular Depth Estimation in Mobile Robotics”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1).