LiDAR-inertial odometry and mapping (LIOAM), which fuses complementary information of a LiDAR and an Inertial Measurement Unit (IMU), is an attractive solution for pose estimation and mapping. In LI-OAM, both pose and velocity are regarded as state variables that need to be solved. However, the widely-used Iterative Closest Point (ICP) algorithm can only provide constraint for pose, while the velocity can only be constrained by IMU pre-integration. As a result, the velocity estimates inclined to be updated accordingly with the pose results. In this paper, we propose LIW-OAM, an accurate and robust LiDAR-inertial-wheel odometry and mapping system, which fuses the measurements from LiDAR, IMU and wheel encoder in a bundle adjustment (BA) based optimization framework. The involvement of a wheel encoder could provide velocity measurement as an important observation, which assists LI-OAM to provide a more accurate state prediction. In addition, constraining the velocity variable by the observation from wheel encoder in optimization can further improve the accuracy of state estimation. Experiment results on two public datasets demonstrate that our system outperforms all state-of-the-art LI-OAM systems in terms of smaller absolute trajectory error (ATE), and embedding a wheel encoder can greatly improve the performance of LI-OAM based on the BA framework.

翻译：激光雷达-惯性里程计与建图（LIOAM）通过融合激光雷达与惯性测量单元（IMU）的互补信息，为位姿估计与建图提供了有吸引力的解决方案。在LIOAM中，位姿和速度均被视为需要求解的状态变量。然而，广泛使用的迭代最近点（ICP）算法仅能为位姿提供约束，而速度仅能通过IMU预积分加以约束，这导致速度估计倾向于随位姿结果同步更新。本文提出LIW-OAM——一种精确鲁棒的激光雷达-惯性-轮式里程计与建图系统，该系统在基于光束法平差（BA）的优化框架中融合激光雷达、IMU与轮式编码器的测量数据。轮式编码器的引入可提供速度测量作为重要观测值，有助于LIOAM实现更精确的状态预测。此外，在优化过程中通过编码器观测值对速度变量施加约束可进一步提升状态估计精度。在两个公开数据集上的实验结果表明，本系统在绝对轨迹误差（ATE）指标上优于所有最先进的LIOAM系统，且嵌入轮式编码器可显著提升基于BA框架的LIOAM性能。