Real-Time Terrain Traversability Analysis and Mapping for Autonomous Robotics in Dynamic Environments: Fusing Appearance- and Geometry-Based Approaches

This paper presents advanced methodologies for real-time terrain analysis and mapping in autonomous robotic systems. The focus is on appearance-based terrain traversability analysis and geometric-based terrain traceability analysis. In the appearance-based approach, an enhanced segmentation model using pixel-based augmentation and 13 unique classes is proposed for reliable terrain classification. Semantic images are projected onto a 2.5D map by transforming two-dimensional image data into a three-dimensional coordinate system. The geometric-based approach involves depth estimation from stereo cameras, employing three Zed-2 cameras and the Depth Sensing application programming interface. The research contributes to improved perception and decision-making capabilities of autonomous robots operating in complex and dynamic environments and also provides a new comprehensive data set named CranfieldTerra. Experimental results validate the effectiveness of the proposed methodologies, demonstrating their potential in various applications, such as search and rescue, agriculture, and exploration. This study establishes a foundation for further advancements in autonomous robotics, enhancing their ability to navigate safely and efficiently in challenging terrains.