安全关键的激光雷达-惯性里程计与在线流形确定性保护级别
Safety-Critical LiDAR-Inertial Odometry with On-Manifold Deterministic Protection Level
AI总结：
本文提出一种安全关键的激光雷达-惯性里程计，通过在线流形确定性状态估计提供确定性保护级别，以提升移动机器人在安全关键场景中的导航安全性。
摘要：
在安全关键场景中，自主导航系统的保护级别对于使移动机器人安全执行任务至关重要。然而，现有针对机器人概率导航系统的研究通常使用有限数据集进行离线准确性评估，并假设结果可应用于未知真实环境。因此，当前自主移动机器人往往缺乏在线安全评估的保护级别。为填补这一空白，我们提出了一种安全关键的激光雷达-惯性里程计（LIO），其基于在线流形确定性状态估计提供确定性保护级别。通过采用未知但有界的假设，我们推导出点云噪声与迭代最近点算法估计不确定性之间的简洁闭式关系。利用这一关系，我们设计了一种在线流形椭球集成员滤波器，并将其实现于LIO系统中。利用集成员滤波器的性质，我们的系统将估计位置的可行集作为确定性保护级别，用作机器人下游自主操作的安全参考。实验结果表明，我们的系统能够为各种环境中的不同机器人提供有效的确定性在线安全参考。
英文摘要：
In safety-critical scenarios, the protection level of the autonomous navigation system is crucial for enabling mobile robots to perform safe tasks. However, existing studies on probabilistic navigation systems for robots usually perform offline accuracy evaluations using limited datasets and assume that the results can be applied to unknown real-world environments. As a result, current autonomous mobile robots often lack protection levels for online safety assessment. To fill this gap, we propose a safety-critical LiDAR-inertial odometry (LIO) that provides deterministic protection levels based on on-manifold deterministic state estimation. By adopting the unknown but bounded assumption, we derive a neat closed-form relationship between point cloud noise and the uncertainty of the estimation from the iterated closest point algorithm. Using this relationship, we design an on-manifold ellipsoidal set-membership filter and implement it within the LIO system. Leveraging the properties of the set-membership filter, our system offers the feasible sets of the estimated locations as the deterministic protection levels, serving as safety references for the robots' downstream autonomous operations. The experimental results show that our system can provide effective deterministic online safety references for diverse robots in various environments.
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