Methods of Terrain Traversability Assessment Based on Image Analysis for Mobile Robots

Abstract

The research highlights critical aspects of autonomous mobile robot navigation in unstructured environments. The main focus is on analyzing modern terrain traversability assessment methods based on visual data, including classical computer vision algorithms and advanced deep learning methods. The work discusses the importance of using RGB-D sensors and robust semantic segmentation systems as fundamental components of the architecture of intelligent decision support systems. Particular attention is paid to a comprehensive approach that combines the geometric analysis of the surface with the semantic understanding of the scene. The tables summarize the role of the methods and compare early and late fusion strategies. In addition to the analysis, the paper gives practical recommendations for deploying deep learning models on embedded robotic platforms (INT8 quantization, data augmentation, self-supervised learning, encoder-decoder architectures). Overall, the research emphasizes the important role of a multimodal approach in ensuring the reliable operation of mobile robots in off-road conditions.