Bundle Adjustment (BA) has been proven to improve the accuracy of the LiDAR mapping. However, the BA method has not yet 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 F2F plane-point BA measurement can be constructed using the same-plane points. The LiDAR BA 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. 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% on the private dataset, respectively. 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：光束法平差（BA）已被证明能提升激光雷达建图的精度。然而，BA方法尚未在航位推算导航系统中得到恰当应用。本文提出了一种用于激光雷达-惯性导航的帧到帧（F2F）光束平差方法，命名为BA-LINS。基于直接帧到帧点云关联，在激光雷达关键帧之间建立同面点关联。因此，利用这些同面点可构建帧到帧的面-点光束法平差测量模型。在因子图优化框架下，激光雷达BA测量与惯性测量单元（IMU）预积分测量实现紧耦合。为进一步提升精度，提出一种有效的激光雷达BA测量自适应协方差估计算法。我们在公开和私有数据集上开展了充分的实际环境实验以验证所提出的BA-LINS。结果表明，BA-LINS的精度优于当前最先进方法。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和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显著提升BA-LINS的精度与鲁棒性。