Mobile robots operating in outdoor environments face the challenge of navigating various terrains with different degrees of difficulty. Therefore, traversability estimation is crucial for safe and efficient robot navigation. Current approaches utilize a robot's driving experience to learn traversability in a self-supervised fashion. However, providing sufficient and diverse experience to the robot is difficult in many practical applications. In this paper, we propose a self-supervised traversability learning method that adapts to challenging terrains with limited prior experience. One key aspect is to enable prioritized learning of scarce yet high-risk terrains by using a risk-sensitive approach. To this end, we train a neural network through a risk-aware instance weighting scheme. Another key aspect is to leverage traversability pseudo-labels on the basis of a self-training scheme. The proposed confidence-regularized self-training generates high-quality pseudo-labels, thereby achieving reliable data augmentation for unexperienced terrains. The effectiveness of the proposed method is verified in extensive real-world experiments, ranging from structured urban environments to complex rugged terrains.

中文翻译：

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通过移动机器人的导航体验学习自我监督的可穿越性：一种具有风险意识的自我训练方法

在室外环境中运行的移动机器人面临着在不同难度的各种地形中导航的挑战。因此，可遍历性估计对于安全高效的机器人导航至关重要。当前的方法利用机器人的驾驶经验以自我监督的方式学习可穿越性。然而，在许多实际应用中，为机器人提供足够且多样化的经验是很困难的。在本文中，我们提出了一种自监督的可遍历性学习方法，该方法可以适应具有有限先验经验的具有挑战性的地形。一个关键方面是通过使用风险敏感的方法来优先学习稀缺但高风险的地形。为此，我们通过风险感知实例加权方案训练神经网络。另一个关键方面是在自我训练方案的基础上利用可遍历性伪标签。所提出的置信正则化自训练生成高质量的伪标签，从而为未经历过的地形实现可靠的数据增强。该方法的有效性在广泛的现实世界实验中得到了验证，从结构化城市环境到复杂崎岖的地形。