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.

翻译：基于三维激光雷达的同步定位与建图技术已成为机器人自主导航的基石。尽管基于特征的SLAM系统通过利用边缘与平面结构取得了显著成果，但其常因特征参数化与估计协方差相关的不一致性估计器而受限。本研究提出一种采用定制化参数化与估计器的改进一致性激光雷达-惯性束调整方法。首先，我们提出采用立体投影表示法对平面与边缘特征进行参数化，并通过全面的可观测性分析验证其与一致性估计器的兼容性。其次，我们基于最大后验概率公式与首次估计雅可比矩阵实现了激光雷达-惯性束调整，以保持系统估计协方差的精确性与可观测特性。最后，我们将所提出的束调整方法应用于激光雷达-惯性里程计系统。