Reliable traversability estimation is crucial for autonomous robots to navigate complex outdoor environments safely. Existing self-supervised learning frameworks primarily rely on positive and unlabeled data; however, the lack of explicit negative data remains a critical limitation, hindering the model's ability to accurately identify diverse non-traversable regions. To address this issue, we introduce a method to explicitly construct synthetic negatives, representing plausible but non-traversable, and integrate them into vision-based traversability learning. Our approach is formulated as a training strategy that can be seamlessly integrated into both Positive-Unlabeled (PU) and Positive-Negative (PN) frameworks without modifying inference architectures. Complementing standard pixel-wise metrics, we introduce an object-centric FPR evaluation approach that analyzes predictions in regions where synthetic negatives are inserted. This evaluation provides an indirect measure of the model's ability to consistently identify non-traversable regions without additional manual labeling. Extensive experiments on both public and self-collected datasets demonstrate that our approach significantly enhances robustness and generalization across diverse environments. The source code and demonstration videos will be publicly available.

翻译：可靠的可通行性估计对于自主机器人在复杂户外环境中安全导航至关重要。现有的自监督学习框架主要依赖于正样本和未标记数据；然而，缺乏明确的负样本数据仍然是一个关键限制，阻碍了模型准确识别多样化不可通行区域的能力。为解决这一问题，我们引入了一种方法来显式构建合成负样本，这些样本代表合理但不可通行的区域，并将其集成到基于视觉的可通行性学习中。我们的方法被表述为一种训练策略，可以无缝集成到正样本-未标记（PU）和正样本-负样本（PN）框架中，而无需修改推理架构。作为标准逐像素评估指标的补充，我们引入了一种以对象为中心的错误正例率评估方法，该方法分析在插入合成负样本区域内的预测结果。这种评估提供了一种间接衡量模型一致识别不可通行区域能力的指标，而无需额外的人工标注。在公开数据集和自收集数据集上进行的大量实验表明，我们的方法显著增强了模型在不同环境中的鲁棒性和泛化能力。源代码和演示视频将公开提供。