论文标题
使用汽车雷达的一种可靠，可靠的方法来估算自我估计
A Credible and Robust approach to Ego-Motion Estimation using an Automotive Radar
论文作者
论文摘要
一致的运动估计对于所有移动自治系统都是基础。尽管这听起来很容易，但通常情况并非如此，因为改变了从视觉，激光雷达或车轮本身获得的环境条件的变化。雷达传感器是挑战性的照明和天气条件，这是一个明显的选择。通常，汽车雷达返回稀疏点云，代表周围环境。由于不稳定和幻影测量，将此信息用于运动估计是具有挑战性的，这导致异常值率很高。我们介绍了一种可靠且强大的概率方法，以根据这些挑战性的雷达测量值估计自我运动。旨在在松散耦合的传感器融合框架中使用。与现有的解决方案相比，对流行的Nuscenes数据集和其他解决方案进行了评估，我们表明我们提出的算法更可信，而不依赖于显式的对应计算。
Consistent motion estimation is fundamental for all mobile autonomous systems. While this sounds like an easy task, often, it is not the case because of changing environmental conditions affecting odometry obtained from vision, Lidar, or the wheels themselves. Unsusceptible to challenging lighting and weather conditions, radar sensors are an obvious alternative. Usually, automotive radars return a sparse point cloud, representing the surroundings. Utilizing this information to motion estimation is challenging due to unstable and phantom measurements, which result in a high rate of outliers. We introduce a credible and robust probabilistic approach to estimate the ego-motion based on these challenging radar measurements; intended to be used within a loosely-coupled sensor fusion framework. Compared to existing solutions, evaluated on the popular nuScenes dataset and others, we show that our proposed algorithm is more credible while not depending on explicit correspondence calculation.
