论文标题
语义大满贯的概率数据关联
Probabilistic Data Association for Semantic SLAM at Scale
论文作者
论文摘要
随着图像处理和机器学习的进步，现在可以将语义信息纳入同时本地化和映射（SLAM）的问题是可行的。以前，使用较低级的几何特征（点，线和平面）进行了SLAM，这些特征通常在视觉重复的环境中易于观点依赖和错误。语义信息可以提高识别先前访问的位置的能力，并维护长期大满贯应用程序的稀疏地图。但是，重复环境中的大满贯有一个关键问题，即将测量值分配给生成它们的地标。在本文中，我们使用K最佳分配枚举来实时计算每个测量地标对的边际分配概率。我们介绍了Kitti数据集的数值研究，以证明所提出的框架的有效性和速度。
With advances in image processing and machine learning, it is now feasible to incorporate semantic information into the problem of simultaneous localisation and mapping (SLAM). Previously, SLAM was carried out using lower level geometric features (points, lines, and planes) which are often view-point dependent and error prone in visually repetitive environments. Semantic information can improve the ability to recognise previously visited locations, as well as maintain sparser maps for long term SLAM applications. However, SLAM in repetitive environments has the critical problem of assigning measurements to the landmarks which generated them. In this paper, we use k-best assignment enumeration to compute marginal assignment probabilities for each measurement landmark pair, in real time. We present numerical studies on the KITTI dataset to demonstrate the effectiveness and speed of the proposed framework.
