Advances and Trends in Terrain Classification Methods for Off-Road Perception

Abstract

Off-road autonomous vehicles (OAVs) are becoming increasingly popular for navigating challenging environments in agriculture, military, and exploration applications. These vehicles face unique challenges, such as unpredictable terrain, dynamic obstacles, and varying environmental conditions. Therefore, it is essential to have an efficient terrain classification system to ensure safe and efficient operation of OAVs. This paper provides an overview of recent advances and emerging trends in off-road terrain classification methods. Through a comprehensive literature review, this study explores the use of sensor modalities and techniques that leverage both appearance and geometry of the terrain for classification tasks. The study discusses learning-based approaches, particularly deep learning, and highlights the integration of multiple sensor modalities through hybrid multimodal techniques. Finally, this study reviews the available off-road datasets and explores the use cases and applications of terrain classification across various autonomous domains. Given the rapid advancements in terrain classification, this paper organizes and surveys to provide a comprehensive overview. By offering a structured review of the current landscape, this paper significantly enhances our understanding of terrain classification in unstructured environments, while also highlighting important areas for future research, particularly in deep-learning-based advancements.