Comprehensive Review of Contact Estimation Methods for Legged Robots

The field of legged robotics has seen significant advances in recent years, with contact detection and state estimation emerging as essential components in achieving robust and adaptive locomotion. This review examines recent developments in contact detection algorithms for legged robots, including model-based, learning-based, and probabilistic approaches, each with unique strengths and limitations. Selecting an appropriate contact estimation strategy requires careful consideration of environmental uncertainties, sensors, and trade-offs between model interpretability and adaptability. Model-based methods excel in controlled environments by offering interpretable and efficient solutions, while probabilistic methods address uncertainties in noisy and dynamic settings, enhancing robustness. Meanwhile, learning-based methods demonstrate better adaptability and terrain generalization, making them ideal for complex and diverse environments. This review presents a comparative analysis of contact detection methods, highlighting their applicability and limitations. Finally, key challenges such as sensor fusion, computational complexity, and terrain adaptability are discussed to guide future research.