为机器人导航设计可在线评估安全性的激光惯性里程计系统
Safety-Critical LiDAR-Inertial Odometry with On-Manifold Deterministic Protection Level
- 基于流形上的集合保形滤波，实现确定性安全边界计算
- 实测表明系统在多种环境与机器人上均能提供有效安全参考
- 适合对安全性要求高的自动驾驶、巡检机器人等场景
在安全关键场景中，自主导航系统的保护水平对于移动机器人执行安全任务至关重要。然而，现有机器人概率导航研究通常使用有限数据集进行离线精度评估，并假设结果可推广至未知真实环境，导致当前自主移动机器人普遍缺乏在线安全评估的保护水平。为此，本文提出一种具备确定性保护水平的安全关键激光惯性里程计（LIO），其基于流形上的确定性状态估计。通过采用未知但有界的假设，推导出点云噪声与迭代最近点算法估计不确定性之间的闭式关系。利用该关系，设计了流形上的椭球集合成员滤波器，并集成于LIO系统中。借助集合成员滤波的性质，系统可输出估计位置的可行集作为确定性保护水平，为机器人下游自主操作提供安全参考。实验结果表明，本系统可在多种环境与不同机器人上提供有效的在线确定性安全参考。
原文摘要 · Abstract (English)
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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