Overview of Terrain Traversability Evaluation for Autonomous Robots

• Published in: Journal of field robotics Vol. 42; no. 5; pp. 1724 - 1765
• Main Authors: Shu, Yongjie, Dong, Linwei, Liu, Jianfeng, Liu, Cheng, Wei, Wei
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.08.2025
• Subjects: Algorithms; Autonomous navigation; autonomous robots; data‐driven; Learning; learning‐based; Robots; Scene analysis; Terrain; terrain characterization; traversability evaluation
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.22461

ABSTRACT Traversability evaluation is the foundation and core of unmanned platforms for scene understanding and autonomous navigation, whose successful completion relies on the analysis of the platform's characteristics and the semantic and geometric features of the surrounding environment. This topic has been reviewed by many literatures, which are characterized by a single perspective and lack comprehensive evaluation frameworks. Thus, the concept and developmental trajectory of traversability evaluation are initially outlined in this paper, distinguishing it from other issues, while constructing an evaluation framework based on two categories: direct assessment and downstream task assessment. Subsequently, traversability evaluation methods are classified based on multiple dimensions, including sensor types, robot types, usage scenarios, and learning approaches. On the basis of the constructed evaluation framework, comparisons are made among existing algorithms in terms of performance and runtime. Subsequently, a summary is provided on commonly used features and their mainstream computation methods in terrain evaluation. Additionally, open‐source data sets in this field and projects for scene construction and algorithm validation are compiled and organized. Finally, an analysis is conducted on the development direction and trends, emphasizing the urgent need to establish standardized evaluation systems and comparison baselines. Furthermore, it is imperative that various environmental and platform information be comprehensively integrated into algorithms, while also ensuring that simulation, demonstration, and exploration are incorporated into a unified framework to enhance the robot's learning capability.