RADARODO: Ego-Motion Estimation From Doppler and Spatial Data in RADAR Images
RADARODO: Ego-Motion Estimation From Doppler and Spatial Data in RADAR Images
复制标题
RADARODO：根据雷达图像中的多普勒和空间数据进行自我运动估计
DOI：
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发表时间：
2020
影响因子：
8.2
通讯作者：
S. Brennan
中科院分区：
文献类型：
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作者：
Chris D. Monaco;S. Brennan
准确和鲁棒的自我运动估计对于帮助自动驾驶汽车在没有地图地标或精确GNSS测量的环境中定位至关重要。雷达测量为自我运动估计提供了独特的能力，同时也提出了独特的挑战。提出了一种新的基于雷达的实时自我运动估计算法RADARODO （RADAR Odometry）。RADARODO与类似技术的区别在于两个新属性。首先，它通过分别从多普勒和空间数据中估计传感器的平移和旋转运动来解耦。其次，直接分析雷达图像，通过无对应非线性优化估计旋转运动；与其他方法不同的是，RADARODO通过单个雷达传感器来估计自我运动及其预测的不确定性，而不依赖于杠杆臂偏移量或零侧滑假设。真实世界的结果证明了精确的自我运动估计，特别适合在“紧密耦合”传感器融合框架内集成。
Accurate and robust ego-motion estimation is critical for aiding autonomous vehicle localization in environments devoid of map landmarks or accurate GNSS measurements. RADAR measurements provide unique capabilities for ego-motion estimation while also posing unique challenges. This paper presents the novel real-time RADAR-based ego-motion estimation algorithm, RADARODO (RADAR Odometry). RADARODO is differentiated from similar techniques by two novel attributes. First, it decouples sensor translational and rotational motion by estimating them from Doppler and spatial data, respectively. Secondly, it directly analyzes RADAR images to estimate rotational motion via correspondence-free non-linear optimization. Unlike other methods, RADARODO estimates ego-motion along with its predicted uncertainty from a single RADAR sensor without depending on a lever-arm offset or the zero side-slip assumption. The real-world results demonstrate accurate ego-motion estimates that are particularly suited for integration within a “tightly-coupled” sensor fusion framework.