改进激光惯性联合优化，提升定位一致性与精度
A Consistency-Improved LiDAR-Inertial Bundle Adjustment
- 用球面投影重参数化平面与边缘特征，增强系统可观测性
- 采用最大后验估计与首估雅可比，保持协方差准确性和一致性
- 适用于高精度机器人自主导航，适合对定位可靠性要求高的场景
基于3D激光雷达的同步定位与地图构建（SLAM）已成为机器人自主导航的核心技术。尽管基于特征的SLAM系统通过利用边缘和平面结构取得了显著成果，但其特征参数化方式常导致估计器不一致及协方差估计失真。本文提出一种一致性改进的激光惯性束调整（BA）方法，采用定制化参数化与估计器设计。首先，引入球面投影表示平面与边缘特征，并进行完整的可观测性分析以支持其与一致估计器的融合；其次，实现基于最大后验（MAP）框架与首估雅可比（FEJ）的激光惯性束调整，有效保留系统协方差的准确性与可观测性特性；最后，将所提方法应用于激光惯性里程计中，验证了其在实际场景中的有效性。
原文摘要 · Abstract (English)
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.
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