LIWO：激光雷达-惯性-轮式里程计
LIWO: Lidar-Inertial-Wheel Odometry
AI总结：
提出LIWO，一种融合激光雷达、IMU和轮式编码器的束调整优化里程计，通过轮式编码器提供速度观测约束，显著提升状态估计精度，在公开数据集上超越现有LIO系统。
摘要：
激光雷达-惯性里程计（LIO）融合了激光雷达和惯性测量单元（IMU）的互补信息，是状态估计的一种有吸引力的解决方案。在LIO中，姿态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）算法只能为姿态提供约束，而速度只能由IMU预积分约束。因此，速度估计倾向于随姿态结果相应更新。在本文中，我们提出了LIWO，一种精确且鲁棒的激光雷达-惯性-轮式（LIW）里程计，它在基于束调整（BA）的优化框架中融合了激光雷达、IMU和轮式编码器的测量。轮式编码器的引入可以提供速度测量作为重要观测，这有助于LIO提供更准确的状态预测。此外，在优化中通过轮式编码器的观测约束速度变量可以进一步提高状态估计的精度。在两个公开数据集上的实验结果表明，我们的系统在较小的绝对轨迹误差（ATE）方面优于所有最先进的LIO系统，并且嵌入轮式编码器可以极大地提高基于BA框架的LIO的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
