等变RO：一种基于等变网络的4D毫米波雷达里程计
Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks
AI总结：
本文提出Equi-RO，一种基于等变网络的4D毫米波雷达里程计方法，通过图结构增强稀疏雷达数据特征聚合，实现更准确的平移和旋转估计。
摘要：
自动驾驶车辆和机器人依赖于在GPS拒止环境中准确的里程计估计。尽管LiDAR和相机在极端天气下表现不佳，4D毫米波雷达作为一种稳健的替代方案，具有全天候操作能力和速度测量能力。在本文中，我们介绍了Equi-RO，一种基于等变网络的4D雷达里程计框架。我们的算法将多普勒速度预处理为图中的不变节点和边特征，并采用分别处理等变和不变特征的网络。基于图的架构增强了稀疏雷达数据中的特征聚合，提高了帧间对应性。在开放源数据集和自收集数据集上的实验表明，Equi-RO在准确性和鲁棒性上优于最先进的算法。总体而言，我们的方法在开放源数据集上相比最佳基线，分别在平移和旋转准确率上实现了10.7%和13.4%的相对提升。
英文摘要：
Autonomous vehicles and robots rely on accurate odometry estimation in GPS-denied environments. While LiDARs and cameras struggle under extreme weather, 4D mmWave radar emerges as a robust alternative with all-weather operability and velocity measurement. In this paper, we introduce Equi-RO, an equivariant network-based framework for 4D radar odometry. Our algorithm pre-processes Doppler velocity into invariant node and edge features in the graph, and employs separate networks for equivariant and invariant feature processing. A graph-based architecture enhances feature aggregation in sparse radar data, improving inter-frame correspondence. Experiments on an open-source dataset and a self-collected dataset show Equi-RO outperforms state-of-the-art algorithms in accuracy and robustness. Overall, our method achieves 10.7% and 13.4% relative improvements in translation and rotation accuracy, respectively, compared to the best baseline on the open-source dataset.
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