Safety-Critical LiDAR-Inertial Odometry with On-Manifold Deterministic Protection Level

2026-05-10

AI Summary

This work addresses the lack of reliable online safety assurance mechanisms in existing robotic navigation systems operating in unknown real-world environments. It proposes a safety-critical LiDAR-inertial odometry (LIO) framework that, for the first time, integrates deterministic ellipsoidal set-membership filtering under manifold constraints into LIO, enabling online computation of deterministic protection levels without relying on probabilistic assumptions. Built upon an unknown-but-bounded noise model, the method establishes a closed-form relationship on the manifold between point cloud measurement noise and state estimation uncertainty by analyzing error propagation in the iterative closest point algorithm and tightly fusing LiDAR and inertial measurements. Experimental results demonstrate that the system delivers real-time, deterministic safety guarantees across diverse environments and platforms, thereby enhancing the safety of downstream autonomous tasks.

Abstract

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.

Problem

• safety-critical
• protection level
• LiDAR-inertial odometry
• online safety assessment
• autonomous navigation

Innovation

• deterministic protection level
• on-manifold estimation
• set-membership filtering
• LiDAR-inertial odometry
• unknown but bounded