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，搭载由非重复扫描激光雷达与全向相机组成的全向视场传感器套件。基于激光雷达的非重复扫描特性，提出一种自动无靶标联合标定方法，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。分别与传统靶标内参标定及基于互信息的外参标定方法进行了对比分析。利用该联合标定传感器套件，混合建图机器人整合了基于里程计的建图模式与静态建图模式。同时提出新的粗到细建图流程：基于里程计建图模式实现大场景的高效粗略建图；基于粗模型对感兴趣区域进行视点规划（依赖前期工作）；导航至各视点并执行更精细精准的静态扫描与建图。最终将精细模型与全局粗模型融合，相较于传统静态方法及新兴的基于里程计方法，可分别获得更高效与精准的结果。