A Consistency-Improved LiDAR-Inertial Bundle Adjustment

TL;DR

This paper introduces a novel LiDAR-inertial bundle adjustment method that enhances consistency and observability in SLAM by using specialized feature parameterization and estimator techniques, improving the reliability of autonomous navigation systems.

Contribution

It proposes a stereographic-projection feature parameterization and a MAP-based BA with FEJ to improve estimator consistency and covariance accuracy in LiDAR-inertial SLAM.

Findings

Enhanced estimator consistency and covariance accuracy.

Improved SLAM robustness in autonomous navigation.

Validated effectiveness on LiDAR-inertial odometry datasets.

Abstract

Simultaneous Localization and Mapping (SLAM) using 3D LiDAR has emerged as a cornerstone for autonomous navigation in robotics. While feature-based SLAM systems have achieved impressive results by leveraging edge and planar structures, they often suffer from the inconsistent estimator associated with feature parameterization and estimated covariance. In this work, we present a consistency-improved LiDAR-inertial bundle adjustment (BA) with tailored parameterization and estimator. First, we propose a stereographic-projection representation parameterizing the planar and edge features, and conduct a comprehensive observability analysis to support its integrability with consistent estimator. Second, we implement a LiDAR-inertial BA with Maximum a Posteriori (MAP) formulation and First-Estimate Jacobians (FEJ) to preserve the accurate estimated covariance and observability properties of the system. Last, we apply our proposed BA method to a LiDAR-inertial odometry.