在雷达SLAM中融入点不确定性
Incorporating Point Uncertainty in Radar SLAM
AI总结：
本文针对雷达SLAM中感知稀疏和噪声问题，提出在极坐标下建模雷达点不确定性并集成到数据关联与后端状态估计中，实验验证了该方法能有效提升雷达SLAM系统性能。
摘要：
雷达SLAM在雾、灰尘和烟雾等挑战性条件下具有鲁棒性，但受限于雷达感知的稀疏性和噪声，包括散斑噪声和多径效应。本研究通过融入点不确定性，提供了一个性能增强的雷达SLAM系统。基础系统是一个雷达-惯性里程计系统，利用速度辅助的雷达点和高频惯性测量。我们首先提出在极坐标下对雷达点的不确定性进行建模，考虑雷达感知的本质。然后，将所提出的不确定性模型集成到数据关联模块中，并用于后端状态估计。在公开数据集和自采集数据集上的真实世界实验验证了所提出模型和方法的有效性。研究结果突显了融入点不确定性以改进雷达SLAM系统的潜力。我们在https://github.com/HKUST-Aerial-Robotics/RIO上公开了代码和收集的数据集。
英文摘要：
Radar SLAM is robust in challenging conditions, such as fog, dust, and smoke, but suffers from the sparsity and noisiness of radar sensing, including speckle noise and multipath effects. This study provides a performance-enhanced radar SLAM system by incorporating point uncertainty. The basic system is a radar-inertial odometry system that leverages velocity-aided radar points and high-frequency inertial measurements. We first propose to model the uncertainty of radar points in polar coordinates by considering the nature of radar sensing. Then, the proposed uncertainty model is integrated into the data association module and incorporated for back-end state estimation. Real-world experiments on both public and self-collected datasets validate the effectiveness of the proposed models and approaches. The findings highlight the potential of incorporating point uncertainty to improve the radar SLAM system. We make the code and collected dataset publicly available at https://github.com/HKUST-Aerial-Robotics/RIO.
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