PA-LVIO: 基于姿态仅捆绑调整的实时激光雷达-视觉-惯性里程计与建图
PA-LVIO: Real-Time LiDAR-Visual-Inertial Odometry and Mapping with Pose-Only Bundle Adjustment
AI总结：
本文提出PA-LVIO，通过姿态仅捆绑调整实现高精度实时激光雷达-视觉-惯性里程计与建图，结合无边际化和帧到地图的激光雷达测量模型，消除里程计漂移，实现高精度点云地图生成。
摘要：
实时激光雷达-视觉-惯性里程计与建图对于智能交通系统中的导航和规划任务至关重要。本文提出一种基于姿态仅捆绑调整（PA）的激光雷达-视觉-惯性里程计（LVIO），称为PA-LVIO，以满足实时导航和建图的迫切需求。所提出的PA框架对于激光雷达和视觉测量具有高度的准确性和效率，并能够在多个帧中推导出可靠的帧到帧约束。一个无边际化和帧到地图（F2M）的激光雷达测量模型被集成到状态估计器中，以消除里程计漂移。同时，采用以惯性测量单元（IMU）为中心的在线空间-时间校准，以获得像素级的激光雷达-相机对齐。通过准确的估计里程计和外参，可以构建高质量的RGB渲染点云地图。在轮式机器人、无人驾驶航空器（UAV）和手持设备上收集的公共和私有数据集上进行了全面实验，共28个序列和超过50公里的轨迹。充分的结果表明，所提出的PA-LVIO在里程计精度和地图质量方面优于或与最先进的LVIO方法相当。此外，PA-LVIO可以在桌面PC和车载ARM计算机上实时运行。代码和数据集已开源在GitHub（https://github.com/i2Nav-WHU/PA-LVIO）上，以造福社区。
英文摘要：
Real-time LiDAR-visual-inertial odometry and mapping is crucial for navigation and planning tasks in intelligent transportation systems. This study presents a pose-only bundle adjustment (PA) LiDAR-visual-inertial odometry (LVIO), named PA-LVIO, to meet the urgent need for real-time navigation and mapping. The proposed PA framework for LiDAR and visual measurements is highly accurate and efficient, and it can derive reliable frame-to-frame constraints within multiple frames. A marginalization-free and frame-to-map (F2M) LiDAR measurement model is integrated into the state estimator to eliminate odometry drifts. Meanwhile, an IMU-centric online spatial-temporal calibration is employed to obtain a pixel-wise LiDAR-camera alignment. With accurate estimated odometry and extrinsics, a high-quality and RGB-rendered point-cloud map can be built. Comprehensive experiments are conducted on both public and private datasets collected by wheeled robot, unmanned aerial vehicle (UAV), and handheld devices with 28 sequences and more than 50 km trajectories. Sufficient results demonstrate that the proposed PA-LVIO yields superior or comparable performance to state-of-the-art LVIO methods, in terms of the odometry accuracy and mapping quality. Besides, PA-LVIO can run in real-time on both the desktop PC and the onboard ARM computer. The codes and datasets are open sourced on GitHub (https://github.com/i2Nav-WHU/PA-LVIO) to benefit the community.
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。
