LiDAR-inertial odometry (LIO), which fuses complementary information of a LiDAR and an Inertial Measurement Unit (IMU), is an attractive solution for state estimation. In LIO, 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 LIWO, an accurate and robust LiDAR-inertialwheel (LIW) odometry, 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 LIO 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 LIO systems in terms of smaller absolute trajectory error (ATE), and embedding a wheel encoder can greatly improve the performance of LIO based on the BA framework.

翻译：摘要：激光雷达-惯性里程计（LIO）融合激光雷达与惯性测量单元（IMU）的互补信息，是一种具有吸引力的状态估计方案。在LIO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预积分进行约束，导致速度估计往往随位姿结果被动更新。本文提出LIWO——一种精确鲁棒的激光雷达-惯性-轮式（LIW）里程计，其通过基于光束法平差（BA）的优化框架融合激光雷达、IMU与轮式编码器的测量数据。引入轮式编码器可直接提供速度测量这一重要观测值，辅助LIO实现更精确的状态预测。此外，在优化过程中通过轮式编码器的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，我们的系统在绝对轨迹误差（ATE）指标上优于所有最先进的LIO系统，且集成轮式编码器能显著提升基于BA框架的LIO性能。