LVI-Q：基于紧耦合高效交替优化的四足机器人鲁棒激光-视觉-惯性-运动学里程计
LVI-Q: Robust LiDAR-Visual-Inertial-Kinematic Odometry for Quadruped Robots Using Tightly-Coupled and Efficient Alternating Optimization
AI总结：
针对四足机器人在复杂环境中的定位漂移问题，提出紧耦合激光-视觉-惯性-运动学里程计系统，通过优化与滤波交替融合多传感器信息，在公开和长期数据集上展现出鲁棒性能。
摘要：
腿式机器人在复杂动态环境中的自主导航依赖于鲁棒的同步定位与建图（SLAM）系统，以准确绘制周围环境并定位机器人，确保安全高效运行。虽然先前基于传感器融合的SLAM方法集成了多种传感器模态以提高鲁棒性，但这些算法由于依赖不合适的融合策略，在具有挑战性的环境中仍易受估计漂移影响。因此，我们提出一种鲁棒的激光-视觉-惯性-运动学里程计系统，集成相机、激光雷达、惯性测量单元（IMU）和关节编码器等多种传感器信息，用于视觉和激光里程计估计。我们的系统采用基于融合的位姿估计方法，根据测量可用性运行基于优化的视觉-惯性-运动学里程计（VIKO）和基于滤波的激光-惯性-运动学里程计（LIKO）。在VIKO中，我们利用足部预积分技术和基于超像素聚类的鲁棒激光-视觉深度一致性，在滑动窗口优化中实现。在LIKO中，我们融入足部运动学，并在误差状态迭代卡尔曼滤波器（ESIKF）中采用点到平面残差。与其他基于传感器融合的SLAM算法相比，我们的方法在公开和长期数据集上展现出鲁棒性能。
英文摘要：
Autonomous navigation for legged robots in complex and dynamic environments relies on robust simultaneous localization and mapping (SLAM) systems to accurately map surroundings and localize the robot, ensuring safe and efficient operation. While prior sensor fusion-based SLAM approaches have integrated various sensor modalities to improve their robustness, these algorithms are still susceptible to estimation drift in challenging environments due to their reliance on unsuitable fusion strategies. Therefore, we propose a robust LiDAR-visual-inertial-kinematic odometry system that integrates information from multiple sensors, such as a camera, LiDAR, inertial measurement unit (IMU), and joint encoders, for visual and LiDAR-based odometry estimation. Our system employs a fusion-based pose estimation approach that runs optimization-based visual-inertial-kinematic odometry (VIKO) and filter-based LiDAR-inertial-kinematic odometry (LIKO) based on measurement availability. In VIKO, we utilize the footpreintegration technique and robust LiDAR-visual depth consistency using superpixel clusters in a sliding window optimization. In LIKO, we incorporate foot kinematics and employ a point-toplane residual in an error-state iterative Kalman filter (ESIKF). Compared with other sensor fusion-based SLAM algorithms, our approach shows robust performance across public and longterm datasets.
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。
