用等变网络提升毫米波雷达里程计精度，适合恶劣天气下的自动驾驶定位。
Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks
- 构建图结构处理雷达数据，分离不变与等变特征提升匹配精度。
- 在公开数据集上翻译和旋转误差分别降低10.7%和13.4%。
- 适用于无卫星信号、极端天气下的机器人与自动驾驶系统。
自动驾驶车辆和机器人依赖于在无GPS环境下的精确里程计估计。尽管激光雷达和摄像头在极端天气下表现不佳，4D毫米波雷达因其全天候工作能力及速度测量优势，成为可靠替代方案。本文提出Equi-RO，一种基于等变网络的4D雷达里程计框架。算法将多普勒速度预处理为图中的不变节点与边特征，并采用独立网络分别处理等变与不变特征。基于图的架构增强了稀疏雷达数据中的特征聚合，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在准确性和鲁棒性上均优于现有先进方法。总体而言，在开源数据集上相比最佳基线，平移误差减少10.7%，旋转误差减少13.4%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。