Abstract
Stereovision is a crucial technique for vision-based obstacle detection as it provides accurate 3D information for analyzing scenes and detecting obstacles. Despite its effectiveness, existing methods often struggle with real-time performance and computational efficiency. This paper introduces a new stereovision-based obstacle detection system that integrates two novel approaches: Optimized Disparity Map Computation (ODMC), which enhances Hirschmüller’s method for faster disparity map generation, and Disparity Seeded Region Growing (D-SRG), an improved version of the SRG algorithm optimized for identifying obstacles in real-time. Our system is designed to address key challenges such as high memory consumption and slow processing speed, enabling efficient real-time processing from image acquisition to obstacle detection using a 3D camera. Experimental results show that:
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