Ægidius, Sebastian; Hadjivelichkov, Dennis; Jiao, Jianhao; Embley-Riches, Jonathan; Kanoulas, Dimitrios; (2025) Watch Your STEPP: Semantic Traversability Estimation Using Pose Projected Features. In: 2025 IEEE International Conference on Robotics and Automation (ICRA). (pp. pp. 2376-2382). IEEE: Atlanta, GA, USA.

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Abstract

Understanding the traversability of terrain is essential for autonomous robot navigation, particularly in unstructured environments such as natural landscapes. Although traditional methods, such as occupancy mapping, provide a basic framework, they often fail to account for the complex mobility capabilities of some platforms such as legged robots. In this work, we propose a method for estimating terrain traversability by learning from demonstrations of human walking. Our approach leverages dense, pixel-wise feature embeddings generated using the DINOv2 vision Transformer model, which are processed through an encoder-decoder MLP architecture to analyze terrain segments. The averaged feature vectors, extracted from the masked regions of interest, are used to train the model in a reconstruction-based framework. By minimizing reconstruction loss, the network distinguishes between familiar terrain with a low reconstruction error and unfamiliar or hazardous terrain with a higher reconstruction error. This approach facilitates the detection of anomalies, allowing a legged robot to navigate more effectively through challenging terrain. We run real-world experiments on the ANYmal legged robot both indoor and outdoor to prove our proposed method. The code is open-source, while video demonstrations can be found on our website: https://rpl-cs-ucl.github.io/STEPP/

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