论文标题
在实时大满贯中比较基于视图和基于地图的语义标签
Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM
论文作者
论文摘要
通常，功能强大的空间AI系统必须构建持久的场景表示，其中几何模型与有意义的语义标签结合在一起。标记场景的许多方法可以分为两个明确的组：基于视图的估算标签，从输入视图数据估算标签，然后将它们逐渐融合到构建场景模型中；基于地图，标记生成的场景模型。但是，到目前为止，尚未尝试定量比较基于视图的标签和基于地图的标签。在这里，我们提出了一个实验框架和比较，该框架和比较使用实时高度图融合作为公平比较的可访问平台，开辟了该领域进一步系统研究的途径。
Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels. The many approaches to labelling scenes can be divided into two clear groups: view-based which estimate labels from the input view-wise data and then incrementally fuse them into the scene model as it is built; and map-based which label the generated scene model. However, there has so far been no attempt to quantitatively compare view-based and map-based labelling. Here, we present an experimental framework and comparison which uses real-time height map fusion as an accessible platform for a fair comparison, opening up the route to further systematic research in this area.
