用高斯过程优化雷达惯性里程计，提升恶劣天气下的定位精度。
Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process
- 基于区域不确定性建模雷达地面点，解决雷达地面拟合失败问题。
- 连续速度预积分使垂直方向漂移低于1%，显著提升高度精度。
- 适合需要高鲁棒性定位的自动驾驶与机器人领域应用。
雷达在恶劣天气下具有强感知能力，但其固有噪声高，导致定位挑战。现有雷达里程计通过剔除异常点、利用多普勒速度或融合惯性测量等方法缓解问题。本文提出两项新改进：一是针对雷达地面点分布不准确的问题，设计基于区域的不确定性感知地面建模方法；二是针对雷达与惯性数据时间异步问题，采用高斯过程构建连续速度预积分，实现3自由度线速度与惯性数据的紧密融合，直接推导出完整的6自由度运动。在公开数据集上验证，该方法垂直方向漂移小于1%，大幅提升了高程估计精度。代码将开源供社区使用：https://github.com/wooseongY/Go-RIO。
原文摘要 · Abstract (English)
Radar ensures robust sensing capabilities in adverse weather conditions, yet challenges remain due to its high inherent noise level. Existing radar odometry has overcome these challenges with strategies such as filtering spurious points, exploiting Doppler velocity, or integrating with inertial measurements. This paper presents two novel improvements beyond the existing radar-inertial odometry: ground-optimized noise filtering and continuous velocity preintegration. Despite the widespread use of ground planes in LiDAR odometry, imprecise ground point distributions of radar measurements cause naive plane fitting to fail. Unlike plane fitting in LiDAR, we introduce a zone-based uncertainty-aware ground modeling specifically designed for radar. Secondly, we note that radar velocity measurements can be better combined with IMU for a more accurate preintegration in radar-inertial odometry. Existing methods often ignore temporal discrepancies between radar and IMU by simplifying the complexities of asynchronous data streams with discretized propagation models. Tackling this issue, we leverage GP and formulate a continuous preintegration method for tightly integrating 3-DOF linear velocity with IMU, facilitating full 6-DOF motion directly from the raw measurements. Our approach demonstrates remarkable performance (less than 1% vertical drift) in public datasets with meticulous conditions, illustrating substantial improvement in elevation accuracy. The code will be released as open source for the community: https://github.com/wooseongY/Go-RIO.
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。