对未知环境的自主探索和地图构建具有广泛的应用价值和重要的现实意义。现有方法多采用距离传感器生成二维栅格地图。红/ 绿/ 蓝深度（red/green/blue-depth，RGB-D）传感器提供环境的颜色和深度信息，从而生成三维（three-dimensional，3D）点云地图，便于人类直观感知。本文提出了一种利用双RGB-D 传感器实现未知室内环境自动探测和测绘的系统方法。通过同步处理RGB-D数据，生成定位点，逐步构建三维点云图和二维栅格地图。紧接着，探索方法被建模为一个部分可观测的马尔科夫决策过程，将局部地图推演和全局边界搜索方法相结合进行自主探索，将动态行为约束用于运动控制。这有效避免了局部最优，保证了探测效果。在单连通和多分支区域的实验表明，该方法具有较好的鲁棒性和较高的效率。

The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate two-dimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.