Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China.
Sensors (Basel). 2022 Jul 7;22(14):5117. doi: 10.3390/s22145117.
Simultaneous localization and mapping (SLAM) system-based indoor mapping using autonomous mobile robots in unknown environments is crucial for many applications, such as rescue scenarios, utility tunnel monitoring, and indoor 3D modeling. Researchers have proposed various strategies to obtain full coverage while minimizing exploration time; however, mapping quality factors have not been considered. In fact, mapping quality plays a pivotal role in 3D modeling, especially when using low-cost sensors in challenging indoor scenarios. This study proposes a novel exploration algorithm to simultaneously optimize exploration time and mapping quality using a low-cost RGB-D camera. Feature-based RGB-D SLAM is utilized due to its various advantages, such as low computational cost and dense real-time reconstruction ability. Subsequently, our novel exploration strategies consider the mapping quality factors of the RGB-D SLAM system. Exploration time optimization factors are also considered to set a new optimum goal. Furthermore, a Voronoi path planner is adopted for reliable, maximal obstacle clearance and fixed paths. According to the texture level, three exploration strategies are evaluated in three real-world environments. We achieve a significant enhancement in mapping quality and exploration time using our proposed exploration strategies compared to the baseline frontier-based exploration, particularly in a low-texture environment.
基于同时定位与地图构建（SLAM）系统的自主移动机器人在未知环境中的室内地图绘制对于许多应用至关重要，例如救援场景、公用隧道监测和室内 3D 建模。研究人员已经提出了各种策略来在最小化探索时间的同时实现全面覆盖；然而，映射质量因素尚未被考虑。事实上，映射质量在 3D 建模中起着关键作用，尤其是在具有挑战性的室内场景中使用低成本传感器时。本研究提出了一种新的探索算法，使用低成本 RGB-D 相机同时优化探索时间和映射质量。由于其具有低计算成本和密集实时重建能力等各种优点，因此使用基于特征的 RGB-D SLAM。随后，我们的新探索策略考虑了 RGB-D SLAM 系统的映射质量因素。还考虑了探索时间优化因素，以设定新的最优目标。此外，还采用 Voronoi 路径规划器进行可靠、最大的障碍物清除和固定路径。根据纹理级别，在三个真实环境中评估了三种探索策略。与基于前沿的基线探索相比，我们使用提出的探索策略在映射质量和探索时间方面都取得了显著的提高，尤其是在低纹理环境中。
