The fusion scheme is crucial to the multi-sensor fusion method that is the promising solution to the state estimation in complex and extreme environments like underground mines and planetary surfaces. In this work, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the CRLB theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves real-time and high estimation accuracy performance despite the challenging environment and poor observations.

翻译：融合方案对于多传感器融合方法至关重要，而后者是应对地下矿井和行星表面等复杂极端环境下状态估计问题的有效解决方案。本文提出一种轻量级基于迭代扩展卡尔曼滤波（iEKF）的LiDAR-惯性里程计系统，该系统采用退化感知与模块化传感器融合流程，仅在检测到退化时，将LiDAR点云及来自另一里程计系统的相对位姿作为更新过程中的观测值。通过克拉美-罗下界（CRLB）理论与仿真测试，证明了本方法相较于使用单一观测值的方法具有更高精度。此外，在具有感知挑战性的数据集上，将所提系统与多种最先进的传感器融合方法进行了对比评估。结果表明，尽管环境恶劣且观测质量不佳，所提系统仍能实现实时且高精度的估计性能。