We design a scheme for laser-inertial odometry and mapping with bundle adjustment (BA-LIOM), which can greatly mitigate the problem of undesired ground warping due to sparsity of laser scans and significantly reduce odometry drift. Specifically, an Inertial measurement unit (IMU)-assisted adaptive voxel map initialization algorithm is proposed and elaborately integrated with the existing framework LIO-SAM, allowing for accurate registration in the beginning of the localization and mapping process. In addition, to accommodate to fast-moving and structure-less scenarios, we design a tightly coupled odometry, which jointly optimizes both the IMU preintegration constraints and scan matching with adaptive voxel maps. The voxels (edge and plane, respectively) are updated with BA optimization. And then the accurate mapping result is obtained by performing local BA. The proposed BA-LIOM is thoroughly assessed using datasets collected from multiple platforms over a variety of environments. Experimental results show the superiority of BA-LIOM over the state-of-the-art methods in robustness and precision, especially for large-scale scenarios. BA-LIOM improves the accuracy of localization by $61\%$ and $73\%$ on the buildings and lawn datasets, respectively, and has a $29\%$ accuracy improvement over LIO-SAM on the KITTI datasets. A supplementary video can be accessed at https://youtu.be/5l4ZFhTc2sw.

我们设计了一种激光惯性里程计和束平差测绘方案（BA-LIOM），该方案可以极大地缓解由于激光扫描稀疏性而导致的地面翘曲问题，并显着减少里程计漂移。具体来说，提出了一种惯性测量单元（IMU）辅助的自适应体素图初始化算法，并与现有框架LIO-SAM精心集成，允许在定位和建图过程开始时进行准确配准。此外，为了适应快速移动和无结构的场景，我们设计了一种紧密耦合的里程计，它联合优化了 IMU 预积分约束和与自适应体素图的扫描匹配。体素（分别是边缘和平面）通过 BA 优化进行更新。然后通过局部BA得到准确的映射结果。所提出的 BA-LIOM 使用从各种环境的多个平台收集的数据集进行了彻底评估。实验结果表明，BA-LIOM 在鲁棒性和精度方面优于最先进的方法，特别是对于大规模场景。 BA-LIOM在建筑物和草坪数据集上的定位精度分别提高了$61\%$和$73\%$，并且在 KITTI 数据集上比 LIO-SAM 的精度提高了$29\%$ 。补充视频可访问 https://youtu.be/5l4ZFhTc2sw。