A Consistency-Improved LiDAR-Inertial Bundle Adjustment

TL;DR

This work tackles estimator inconsistency in LiDAR-inertial SLAM caused by problematic feature parameterizations and covariance handling. It introduces a stereographic-projection parameterization for edge and planar features and develops a MAP-based LiDAR-inertial BA with FEJ to maintain the correct observability and covariance in a sliding-window optimization. The approach is integrated into a real-time LIO system with a dedicated front end for feature extraction and a back end that performs joint optimization, including loop closure. Key contributions include the SP representation that avoids singularities, rigorous observability analysis, and a consistency-preserving BA framework that improves drift behavior in LiDAR-inertial odometry.

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.

Figures (3)

• Figure 1: Stereographic Projection of $\bm{\bar{E}}_k$
• Figure 2: System Overview
• Figure 3: Joint Optimization