Abstract: The rapid expansion of ground mobile robots from structured urban roadways to unstructured outdoor environments has increased their demand across diverse domains, including resource exploration, planetary exploration, agricultural cultivation, disaster rescue, and field research, with terrain perception emerging as a core technology for ensuring high mobility and safe autonomous operation of wheeled and tracked robots. In off-road scenarios characterized by the absence of prior environmental models, highly heterogeneous terrain mechanical properties, and significant variations in physical attributes, the current critical bottleneck is in constructing interpretable terrain perception models and achieving effective mapping from heterogeneous observation spaces to robust decision control. Robots must not only identify terrain categories but also quantify the influence of terrain on their dynamic behavior, providing essential inputs for real-time decision-making and adaptive control. This paper systematically reviews the research progress in terrain perception for ground mobile robots, establishing a comprehensive technological chain from multisource perception, semantic and physical property estimation, and traversability assessment to summarize the application of terrain perception in decision planning and motion control in unstructured environments. Methodologically, three mainstream technological approaches can be summarized: first, terrain perception based on exteroceptive information from visual cameras, LiDAR, and millimeter-wave radar to achieve prospective analysis of terrain geometric features and semantic information; second, terrain perception based on interoceptive information from inertial measurement units and drive torque to realize real-time estimation of ground mechanical properties through dynamic response; and third, cross-modal terrain perception integrating heterogeneous sensor information to achieve unified representation of semantic and physical properties, thereby directly linking environmental understanding with motion planning and execution. The paper first analyzes the complex characteristics of unstructured terrain, including irregular surface geometric morphology, nonuniform soil mechanical properties, and environmental uncertainty, to elucidate how these factors affect robot motion stability, traction, and energy consumption. Unlike structured road environments, off-road terrain induces elastic and plastic deformation, subsidence, slippage, and complex wheel-ground interactions, all of which must be considered in high-level decision-making and low-level control. Regarding algorithmic evolution, this paper encompasses the complete spectrum from classical machine learning based on feature engineering to advanced deep learning models, discussing semantic segmentation methods that utilize convolutional neural networks, fully convolutional networks, and vision Transformers, as well as data-driven frameworks to estimate key physical properties, such as friction coefficient, stiffness, cohesion, and sinkage from proprioceptive signals or fused multimodal data. Combined with typical datasets, including RELLIS-3D, RUGD, GOOSE, YCOR, and TartanDrive 2.0, this paper analyzes the critical role of multimodal synchronized data in supporting the joint learning of semantic and physical properties to bridge the gap between perception and executable control. The research focuses on the mechanisms of terrain perception technology in decision planning and motion control, including traversability map construction, cost map generation, terrain-aware velocity planning and adaptive trajectory optimization, and methods for integrating semantic and physical property maps into decision planning and motion control. The integration of these methods significantly enhances system real-time performance, robustness, and energy-efficient mobility capabilities. Research indicates that improving the adaptability of ground mobile robots to complex terrains depends fundamentally on addressing the cognitive uncertainty inherent in unstructured environments. A key approach involves the development of multimodal perception fusion frameworks and closed-loop models that tightly integrate perception, planning, and control. Recent innovations in end-to-end integration, such as deep reinforcement learning for terrain-adaptive strategies, Transformer architectures directly generating trajectories from raw multimodal inputs, and semi-supervised or self-supervised methods addressing sparse annotation, are driving the development in this field. Finally, this paper identifies cross-domain model transfer, dynamic environment adaptation, real-time computational constraints, and heterogeneous sensor stream integration mechanisms as major ongoing challenges and envisions future directions for achieving truly all-terrain autonomous mobility.