论文标题
RGB-D大满贯，结构规律
RGB-D SLAM with Structural Regularities
论文作者
论文摘要
这项工作提出了一个专门为结构化环境设计的RGB-D SLAM系统，并通过依靠从周围环境中提取的几何特征来提高跟踪和映射精度。结构化环境除了点外，还提供了大量的几何特征，例如线条和平面，我们利用这些特征来设计SLAM系统的跟踪和映射组件。对于跟踪部分，我们根据曼哈顿世界（MW）的假设探索这些特征之间的几何关系。我们提出了一种基于点，线和平面的解耦 - 再填充方法，以及在附加的姿势改进模块中使用曼哈顿关系。对于映射部分，从稀疏到密集的不同级别的地图以低计算成本重建。我们提出了一个通过实例的网格划分策略，通过独立的网格平面实例来构建密集地图。在公共基准测试中评估了姿势估计和重建方面的总体表现，与最新方法相比，性能提高。该代码在\ url {https://github.com/yanyan-li/planarslam}发布
This work proposes a RGB-D SLAM system specifically designed for structured environments and aimed at improved tracking and mapping accuracy by relying on geometric features that are extracted from the surrounding. Structured environments offer, in addition to points, also an abundance of geometrical features such as lines and planes, which we exploit to design both the tracking and mapping components of our SLAM system. For the tracking part, we explore geometric relationships between these features based on the assumption of a Manhattan World (MW). We propose a decoupling-refinement method based on points, lines, and planes, as well as the use of Manhattan relationships in an additional pose refinement module. For the mapping part, different levels of maps from sparse to dense are reconstructed at a low computational cost. We propose an instance-wise meshing strategy to build a dense map by meshing plane instances independently. The overall performance in terms of pose estimation and reconstruction is evaluated on public benchmarks and shows improved performance compared to state-of-the-art methods. The code is released at \url{https://github.com/yanyan-li/PlanarSLAM}
