论文标题
安全机器人通过多模式异常检测导航
Safe Robot Navigation via Multi-Modal Anomaly Detection
论文作者
论文摘要
自然室外环境中的导航需要一种可靠且可靠的遍历性分类方法来处理机器人可能会遇到的大量情况。二进制分类算法在其本地域中表现良好，但是在呈现分布式样本时倾向于提供过度自信的预测，这在导航未知环境时可能会导致灾难性失败。我们建议通过在多模式图像上使用异常检测来克服这个问题，以进行遍历性分类，这可以通过机器人体验以自我监督的方式进行训练，可以轻松扩展。在这项工作中，我们评估了多种异常检测方法，并在其来自不同环境条件的数据的性能中结合了独立图像和多模式图像。我们的结果表明，使用特征提取器并使用RGB，深度和表面正态输入的方法可以表现最佳。它在ROC曲线下达到了超过95％的面积，并且对分布样品非常强大。
Navigation in natural outdoor environments requires a robust and reliable traversability classification method to handle the plethora of situations a robot can encounter. Binary classification algorithms perform well in their native domain but tend to provide overconfident predictions when presented with out-of-distribution samples, which can lead to catastrophic failure when navigating unknown environments. We propose to overcome this issue by using anomaly detection on multi-modal images for traversability classification, which is easily scalable by training in a self-supervised fashion from robot experience. In this work, we evaluate multiple anomaly detection methods with a combination of uni- and multi-modal images in their performance on data from different environmental conditions. Our results show that an approach using a feature extractor and normalizing flow with an input of RGB, depth and surface normals performs best. It achieves over 95% area under the ROC curve and is robust to out-of-distribution samples.
