This paper presents novel strategies for spawning and fusing submaps within an elastic dense 3D reconstruction system. The proposed system uses spatial understanding of the scanned environment to control memory usage growth by fusing overlapping submaps in different ways. This allows the number of submaps and memory consumption to scale with the size of the environment rather than the duration of exploration. By analysing spatial overlap, our system segments distinct spaces, such as rooms and stairwells on the fly during exploration. Additionally, we present a new mathematical formulation of relative uncertainty between poses to improve the global consistency of the reconstruction. Performance is demonstrated using a multi-floor multi-room indoor experiment, a large-scale outdoor experiment and simulated datasets. Relative to our baseline, the presented approach demonstrates improved scalability and accuracy.
翻译:本文介绍了在弹性密度3D重建系统中产卵和引信子测绘的新战略,拟议的系统利用对扫描环境的空间理解,以不同方式将重叠的子测绘以不同方式引信的形式对记忆使用量增长进行控制,这样子测绘和内存消耗的数量可以与环境大小相比,而不是与勘探期间的长度成比例;通过分析空间重叠,我们的系统各部分有不同的空间,如勘探期间的室内和飞行楼梯;此外,我们提出了一种新的数学公式公式,说明在改善全球重建一致性方面,各种压力之间的相对不确定性;使用多层多层室内实验、大型室外实验和模拟数据集来显示绩效。与我们的基线相比,所提出的方法显示了更高的可缩放性和准确性。