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

TL;DR

This paper introduces a safety-critical LiDAR-inertial odometry system that provides deterministic protection levels for online safety assessment in autonomous robots, addressing limitations of probabilistic methods.

Contribution

It develops an on-manifold ellipsoidal set-membership filter based on bounded noise assumptions, offering real-time safety references for autonomous navigation.

Findings

System provides effective deterministic safety references in diverse environments.

Closed-form relationship between point cloud noise and estimation uncertainty.

Experimental results validate the system's safety assessment capabilities.

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.