Accurate and robust localization is a critical requirement for autonomous driving and intelligent robots, particularly in complex dynamic environments and various motion scenarios. However, existing LiDAR odometry methods often struggle to promptly respond to changes in the surroundings and motion conditions with fixed parameters through execution, hindering their ability to adaptively adjust system model parameters. Additionally, current localization techniques frequently overlook the confidence level associated with their pose results, leading the autonomous systems to unconditionally accept estimated outputs, even when they may be erroneous. In this paper, we propose a robust data-model dual-driven fusion with uncertainty estimation for the LiDAR–IMU localization system, which integrates the advantages of data-driven and model-driven approaches. We introduce data-driven feature encoder modules for LiDAR and IMU raw data, enabling the system to detect changes in the environment and motion status. Subsequently, these data-driven findings are incorporated into a filtering based model, allowing for the adaptive refinement of system model parameters. Furthermore, we refine the representation of uncertainty based on the Extended-Kalman-Filter model covariance, integrating uncertainty from sensor data and model parameters, which helps to evaluate the confidence of fusion system results. We conducted extensive experiments on two publicly available datasets and one dataset we collected with three sequences, verifying the accuracy of our method. In addition, we have demonstrated the robustness of our method in different motion states and scenarios through comparative experiments, as well as the effectiveness of our refined uncertainty estimation, compared with existing great methods, such as Fast-LIO2 and LIO-SAM, the localization accuracy has been improved by 8.3%.

中文翻译：

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用于 LiDAR-IMU 定位系统的具有不确定性估计的鲁棒数据模型双驱动融合

准确而鲁棒的定位是自动驾驶和智能机器人的关键要求，特别是在复杂的动态环境和各种运动场景中。然而，现有的激光雷达测距方法往往难以通过执行以固定参数快速响应周围环境和运动条件的变化，从而阻碍了其自适应调整系统模型参数的能力。此外，当前的定位技术经常忽略与其姿态结果相关的置信度，导致自主系统无条件接受估计的输出，即使它们可能是错误的。在本文中，我们为 LiDAR-IMU 定位系统提出了一种具有不确定性估计的鲁棒数据模型双驱动融合，它集成了数据驱动和模型驱动方法的优点。我们为 LiDAR 和 IMU 原始数据引入了数据驱动的特征编码器模块，使系统能够检测环境和运动状态的变化。随后，这些数据驱动的发现被纳入基于过滤的模型中，从而允许系统模型参数的自适应细化。此外，我们基于扩展卡尔曼滤波器模型协方差改进了不确定性的表示，整合了传感器数据和模型参数的不确定性，这有助于评估融合系统结果的置信度。我们对两个公开可用的数据集和一个我们收集的三个序列的数据集进行了广泛的实验，验证了我们方法的准确性。此外，与现有的优秀方法（例如 Fast-LIO2 和 LIO-SAM）相比，我们通过对比实验证明了我们的方法在不同运动状态和场景下的鲁棒性，以及我们改进的不确定性估计的有效性。准确率提高了8.3%。