论文标题
组成可伸缩的对象猛击
Compositional Scalable Object SLAM
论文作者
论文摘要
我们提出了一个快速，可扩展，准确的同时定位和映射（SLAM）系统，该系统代表室内场景作为对象图。利用可识别物体结构和占用的人造环境的观察，我们表明，组成可伸缩的对象映射公式可以适合强大的大满贯解决方案，以进行无漂移的大型大规模室内重建。为了实现这一目标，我们提出了一种新颖的语义辅助数据关联策略，该策略获得了明确的持久对象标志，以及一种2.5D组成渲染方法，可实现可靠的框架到模型RGB-D跟踪。因此，我们提供了优化的在线实施，该实施可以通过一张图形卡以接近框架的速度运行，并针对最新基线的状态提供全面的评估。将在https：//占位符中提供开源实施。
We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. Leveraging the observation that artificial environments are structured and occupied by recognizable objects, we show that a compositional scalable object mapping formulation is amenable to a robust SLAM solution for drift-free large scale indoor reconstruction. To achieve this, we propose a novel semantically assisted data association strategy that obtains unambiguous persistent object landmarks, and a 2.5D compositional rendering method that enables reliable frame-to-model RGB-D tracking. Consequently, we deliver an optimized online implementation that can run at near frame rate with a single graphics card, and provide a comprehensive evaluation against state of the art baselines. An open source implementation will be provided at https://placeholder.
