This paper proposes a novel LiDAR-inertial odometry (LIO), named SR-LIO, based on an improved bundle adjustment (BA) framework. The core of our SR-LIO is a novel sweep reconstruction method, which segments and reconstructs raw input sweeps from spinning LiDAR to obtain reconstructed sweeps with higher frequency. Such method can effectively reduce the time interval for each IMU pre-integration, reducing the IMU pre-integration error and enabling the usage of BA based LIO optimization. In order to make all the states during the period of a reconstructed sweep can be equally optimized, we further propose multi-segment joint LIO optimization, which allows the state of each sweep segment to be constrained from both LiDAR and IMU. Experimental results on three public datasets demonstrate that our SR-LIO outperforms all existing state-of-the-art methods on accuracy, and reducing the IMU pre-integration error via the proposed sweep reconstruction is very importance for the success of a BA based LIO framework. The source code of SR-LIO is publicly available for the development of the community.
翻译:本文根据改进的捆绑调整框架,提出了名为SR-LIO的新型LIDO(LIO),名为SR-LIO。我们SR-LIO的核心是一种新型的扫荡重建方法,它从旋转的LIDAR中分离并重建原始输入扫描,以获得更频繁的再扫描。这种方法可以有效地减少每个IMU在整合前的时间间隔,减少IMU在整合前的误差,并能够使用基于BA的LIO优化。为了在重建的扫荡期间使所有各州都能同样得到优化,我们进一步提出了多部分联合LIO优化。这使得每个扫荡段的状态都能够受到LIDAR和IMU的制约。三个公共数据集的实验结果表明,我们的SR-LIO在准确性方面超越了所有现有的最新方法,并通过拟议的清理重建减少IMU在整合前的错误。SR-LIO的源码对于基于BA LIO的框架的成功非常重要。