无需标注数据，实时识别机器人行进中的地形边界。
Temporally Consistent Unsupervised Segmentation for Mobile Robot Perception
- 用DINOv2提取超像素特征，无监督聚类分割地形。
- 通过帧间一致性约束，保持地形边界稳定不变。
- 适用于野外非结构化环境，适合移动机器人导航。
地面自主导航的快速发展依赖于监督语义分割技术，但这类方法需昂贵的数据采集和人工标注。在未预演、非结构化环境中，标签数据缺失且语义类别模糊或领域特定。现有零样本无监督分割方法虽有潜力，但通常仅处理单帧图像，缺乏时间一致性，影响感知鲁棒性。为此，我们提出Frontier-Seg，一种针对移动机器人视频流的时序一致无监督地形分割方法。该方法基于基础模型DINOv2提取超像素级特征，并通过跨帧一致性约束，实现对持久地形边界或前沿的无监督识别。我们在RUGD和RELLIS-3D等多样基准数据集上验证了该方法，证明其可在非结构化越野环境中实现有效的无监督分割。
原文摘要 · Abstract (English)
Rapid progress in terrain-aware autonomous ground navigation has been driven by advances in supervised semantic segmentation. However, these methods rely on costly data collection and labor-intensive ground truth labeling to train deep models. Furthermore, autonomous systems are increasingly deployed in unrehearsed, unstructured environments where no labeled data exists and semantic categories may be ambiguous or domain-specific. Recent zero-shot approaches to unsupervised segmentation have shown promise in such settings but typically operate on individual frames, lacking temporal consistency-a critical property for robust perception in unstructured environments. To address this gap we introduce Frontier-Seg, a method for temporally consistent unsupervised segmentation of terrain from mobile robot video streams. Frontier-Seg clusters superpixel-level features extracted from foundation model backbones-specifically DINOv2-and enforces temporal consistency across frames to identify persistent terrain boundaries or frontiers without human supervision. We evaluate Frontier-Seg on a diverse set of benchmark datasets-including RUGD and RELLIS-3D-demonstrating its ability to perform unsupervised segmentation across unstructured off-road environments.
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