一种无需传感器特定建模的鲁棒激光雷达-惯性里程计方法
A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
AI总结：
本文提出一种无需传感器特定建模的鲁棒激光雷达-惯性里程计方法，通过简化惯性测量单元积分模型和直接扫描到地图的激光雷达配准，提升整体里程计性能，并在多种机器人传感器和平台数据集上验证了其鲁棒性。
摘要：
准确的里程计是机器人导航堆栈中的关键组件，后续模块如规划和控制通常依赖于对机器人运动的估计。基于传感器的里程计方法应能够在不同传感器类型和目标领域中保持鲁棒性，从安装在汽车上的固态激光雷达在城市驾驶场景中到用于无结构自然环境的手持包上的旋转激光雷达。本文提出了一种无需依赖传感器特定建模的鲁棒激光雷达-惯性里程计系统。激光雷达和惯性测量单元（IMU）数据的传感器融合技术通常在卡尔曼滤波中迭代整合IMU数据或在因子图框架中使用预整合，结合激光雷达扫描匹配通常利用某些形式的特征提取。我们提出了一种替代策略，仅需要简化惯性测量单元积分模型，并直接在扫描到地图的方法中配准激光雷达扫描。我们的方法允许我们在激光雷达配准上施加一种新的正则化，提高整体里程计性能。我们详细描述了在涵盖广泛常用机器人传感器和平台的数据集上的大量实验。我们展示了我们的方法在所有这些场景中使用完全相同的配置，证明了其鲁棒性。我们已经开源了我们的实现，以便社区可以进一步基于我们的工作构建并用于他们的导航堆栈。
英文摘要：
Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estimate of the robot's motion. Sensor-based odometry approaches should be robust across sensor types and deployable in different target domains, from solid-state LiDARs mounted on cars in urban-driving scenarios to spinning LiDARs on handheld packages used in unstructured natural environments. In this paper, we propose a robust LiDAR-inertial odometry system that does not rely on sensor-specific modeling. Sensor fusion techniques for LiDAR and inertial measurement unit (IMU) data typically integrate IMU data iteratively in a Kalman filter or use pre-integration in a factor graph framework, combined with LiDAR scan matching often exploiting some form of feature extraction. We propose an alternative strategy that only requires a simplified motion model for IMU integration and directly registers LiDAR scans in a scan-to-map approach. Our approach allows us to impose a novel regularization on the LiDAR registration, improving the overall odometry performance. We detail extensive experiments on a number of datasets covering a wide array of commonly used robotic sensors and platforms. We show that our approach works with the exact same configuration in all these scenarios, demonstrating its robustness. We have open-sourced our implementation so that the community can build further on our work and use it in their navigation stacks.
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。
