论文标题
在雷达下：学习预测雷达中的探测仪估计和度量的强大关键点
Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar
论文作者
论文摘要
本文提出了一个自我监督的框架，用于学习检测雷达中的探测器估计和度量定位的强大关键。通过在架构中嵌入一个基于可分点的运动估计器，我们仅从本地化误差中学习关键点位置，分数和描述符。这种方法避免了对成为强大关键的原因的任何假设，并且至关重要的是可以为我们的应用进行优化。此外，该体系结构是传感器不可知论的，可以应用于大多数模式。我们在280公里的现实世界中进行实验，从牛津雷达机器人数据集驾驶，并改善基于点的雷达探光仪的最先进的实验，从而使误差最多减少了45％，同时又可以更快地运行数量级。结合了这些输出，我们提供了一个能够在城市环境中与雷达进行完整映射和本地化的框架。
This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable point-based motion estimator inside our architecture, we learn keypoint locations, scores and descriptors from localisation error alone. This approach avoids imposing any assumption on what makes a robust keypoint and crucially allows them to be optimised for our application. Furthermore the architecture is sensor agnostic and can be applied to most modalities. We run experiments on 280km of real world driving from the Oxford Radar RobotCar Dataset and improve on the state-of-the-art in point-based radar odometry, reducing errors by up to 45% whilst running an order of magnitude faster, simultaneously solving metric loop closures. Combining these outputs, we provide a framework capable of full mapping and localisation with radar in urban environments.
