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.

翻译：自动驾驶车辆和机器人在GPS拒止环境中依赖于精确的里程计估计。虽然激光雷达和摄像头在极端天气下性能受限，但4D毫米波雷达凭借其全天候工作能力和速度测量优势，成为一种鲁棒的替代方案。本文提出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与边特征，并采用独立的网络分别处理等变特征与不变特征。基于图的架构增强了稀疏雷达数据中的特征聚合能力，从而改善了帧间对应关系。在开源数据集和自采数据集上的实验表明，Equi-RO在精度与鲁棒性上均优于现有先进算法。总体而言，在开源数据集上，与最佳基线方法相比，我们的方法在平移与旋转精度上分别实现了10.7%和13.4%的相对提升。