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.

翻译：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与规划任务至关重要。本研究提出一种基于仅位姿光束平差（PA）的激光雷达-视觉-惯性里程计（LVIO），命名为PA-LVIO，以应对实时导航与建图的迫切需求。所提出的针对激光雷达与视觉测量的PA框架具有高精度与高效率，能够在多帧内推导出可靠的帧间约束。状态估计器中集成了无边缘化且基于帧到图（F2M）的激光雷达测量模型，以消除里程计漂移。同时，采用以IMU为中心的在线时空标定方法，实现逐像素的激光雷达-相机对齐。凭借精确估计的里程计与外参，可构建高质量且经RGB着色的点云地图。在轮式机器人、无人机（UAV）及手持设备采集的公开与私有数据集上进行了全面实验，包含28个序列及超过50公里的轨迹。充分结果表明，所提出的PA-LVIO在里程计精度与建图质量方面相较于最先进的LVIO方法表现出更优或相当的性能。此外，PA-LVIO可在台式电脑与机载ARM计算机上实时运行。代码与数据集已在GitHub（https://github.com/i2Nav-WHU/PA-LVIO）上开源，以造福社区。