稀疏3D点云地图上采样和噪声去除作为vSLAM后处理步骤：实验评估
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
摘要：
基于单目视觉的同时定位与地图构建（vSLAM）是移动机器人和计算机视觉中最具挑战性的问题之一。在这项工作中，我们研究的后处理技术应用于稀疏的三维点云地图，获得基于特征的vSLAM算法。地图后处理分为2个主要步骤：1）噪声和离群值去除和2）上采样。我们评估了已知算法的不同组合，用于对真实室内和室外环境的数据集进行离群值去除和上采样，并确定最有希望的组合。我们进一步使用它来转换点云地图，由真正的无人机进行室内飞行获得的三维体素网格（三维地图）可能适合于路径规划。
英文摘要：
The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision. In this work we study the post-processing techniques applied to sparse 3D point-cloud maps, obtained by feature-based vSLAM algorithms. Map post-processing is split into 2 major steps: 1) noise and outlier removal and 2) upsampling. We evaluate different combinations of known algorithms for outlier removing and upsampling on datasets of real indoor and outdoor environments and identify the most promising combination. We further use it to convert a point-cloud map, obtained by the real UAV performing indoor flight to 3D voxel grid (octo-map) potentially suitable for path planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
