Bundle Adjustment (BA) has been proven to improve the accuracy of the LiDAR mapping. However, the BA method has not been properly employed in a dead-reckoning navigation system. In this paper, we present a frame-to-frame (F2F) BA for LiDAR-inertial navigation, named BA-LINS. Based on the direct F2F point-cloud association, the same-plane points are associated among the LiDAR keyframes. Hence, the plane-point BA measurement can be constructed using the same-plane points. The LiDAR BA measurements and the inertial measurement unit (IMU)-preintegration measurements are tightly integrated under the framework of factor graph optimization. An effective adaptive covariance estimation algorithm for LiDAR BA measurements is proposed to further improve the accuracy of BA-LINS. We conduct exhaustive real-world experiments on public and private datasets to examine the proposed BA-LINS. The results demonstrate that BA-LINS yields superior accuracy to state-of-the-art methods. Compared to the baseline system FF-LINS, the absolute translation accuracy and state-estimation efficiency of BA-LINS are improved by 29.5% and 28.7%, respectively, on the private dataset. Besides, the ablation experiment results exhibit that the proposed adaptive covariance estimation algorithm can notably improve the accuracy and robustness of BA-LINS.

翻译：摘要：光束法平差已被证明能够提升激光雷达建图的精度。然而，BA方法尚未被妥善应用于递推导航系统中。本文提出一种面向激光雷达-惯性导航的帧间光束法平差方法，命名为BA-LINS。基于直接帧间点云关联，激光雷达关键帧之间的同平面点得以建立关联。由此，可利用同平面点构建平面-点BA观测模型。在因子图优化框架下，激光雷达BA观测与惯性测量单元预积分观测被紧密融合。为进一步提升BA-LINS精度，本文提出一种有效的激光雷达BA自适应协方差估计算法。我们在公开数据集与私有数据集上开展了详尽的真实世界实验以验证所提出的BA-LINS。结果表明，BA-LINS的精度优于现有最先进方法。与基线系统FF-LINS相比，BA-LINS在私有数据集上的绝对平移精度与状态估计效率分别提升了29.5%和28.7%。此外，消融实验结果表明，所提出的自适应协方差估计算法能够显著提升BA-LINS的精度与鲁棒性。