This paper presents a novel 3D mapping robot with an omnidirectional field-of-view (FoV) sensor suite composed of a non-repetitive LiDAR and an omnidirectional camera. Thanks to the non-repetitive scanning nature of the LiDAR, an automatic targetless co-calibration method is proposed to simultaneously calibrate the intrinsic parameters for the omnidirectional camera and the extrinsic parameters for the camera and LiDAR, which is crucial for the required step in bringing color and texture information to the point clouds in surveying and mapping tasks. Comparisons and analyses are made to target-based intrinsic calibration and mutual information (MI)-based extrinsic calibration, respectively. With this co-calibrated sensor suite, the hybrid mapping robot integrates both the odometry-based mapping mode and stationary mapping mode. Meanwhile, we proposed a new workflow to achieve coarse-to-fine mapping, including efficient and coarse mapping in a global environment with odometry-based mapping mode; planning for viewpoints in the region-of-interest (ROI) based on the coarse map (relies on the previous work); navigating to each viewpoint and performing finer and more precise stationary scanning and mapping of the ROI. The fine map is stitched with the global coarse map, which provides a more efficient and precise result than the conventional stationary approaches and the emerging odometry-based approaches, respectively.

翻译：本文提出一种新型三维建图机器人，其搭载由非重复扫描激光雷达与全向相机组成的全视场角传感器套件。利用激光雷达的非重复扫描特性，我们提出一种自动无靶标共标定方法，可同时标定全向相机的内参数与相机-激光雷达的外参数，这对于测绘任务中为点云赋予色彩与纹理信息的关键步骤至关重要。分别以基于靶标的内参标定与基于互信息的外参标定作为对比基准，进行了详细分析与比较。基于该共标定传感器套件，混合建图机器人集成了里程计建图模式与静态建图模式。同时，我们提出一种实现粗到精建图的新工作流程：首先通过里程计建图模式在全局环境中进行高效粗建图；随后基于该粗建图规划感兴趣区域（ROI）中的视点（依赖于先前工作）；接着导航至各视点，对ROI进行更精细、精准的静态扫描与建图。最终将精建图与全局粗建图进行拼接，相较于传统静态方法与新兴里程计方法，所提方法能分别实现更高效率与更精确的建图结果。