论文标题
分布式一致的多机器人语义本地化和映射
Distributed Consistent Multi-Robot Semantic Localization and Mapping
论文作者
论文摘要
我们提出了一种在未知环境中一致的多机器人一致的分布式定位和语义映射的方法，考虑到具有分类歧义的情况，其中对象的视觉外观通常随视点而变化。我们的方法通过维持关于连续本地化和离散分类变量的分布式后混合信念来解决这种设置。特别是，我们利用一个依赖观点的分类器模型来利用语义和几何形状之间的耦合。此外，我们的方法对连续变量和离散变量都始终如一地估计，而后者首次解决了我们的最大知识。我们在多机器人语义大满贯模拟和现实世界实验中评估了方法的性能，与仅使用本地信息保持混合信念相比，分类和本地化精度的提高。
We present an approach for multi-robot consistent distributed localization and semantic mapping in an unknown environment, considering scenarios with classification ambiguity, where objects' visual appearance generally varies with viewpoint. Our approach addresses such a setting by maintaining a distributed posterior hybrid belief over continuous localization and discrete classification variables. In particular, we utilize a viewpoint-dependent classifier model to leverage the coupling between semantics and geometry. Moreover, our approach yields a consistent estimation of both continuous and discrete variables, with the latter being addressed for the first time, to the best of our knowledge. We evaluate the performance of our approach in a multi-robot semantic SLAM simulation and in a real-world experiment, demonstrating an increase in both classification and localization accuracy compared to maintaining a hybrid belief using local information only.
