论文标题
语义大满贯与自主对象级数据关联
Semantic SLAM with Autonomous Object-Level Data Association
论文作者
论文摘要
通常希望在同时定位和映射（SLAM）期间捕获和映射环境的语义信息。这样的语义信息可以使机器人能够更好地区分具有相似低级几何和视觉特征的地方，并执行高级任务，这些任务使用有关要操纵的对象以及要导航的环境的语义信息。尽管语义大满贯引起了人们的关注，但基于语义对象（即对象级数据关联）对语义数据关联的研究很少。在本文中，我们提出了一种基于单词算法袋的新颖对象级数据关联算法，该算法被认为是最大加权双分部分匹配问题。通过求解对象级数据关联，我们使用双Quadric开发了基于二次编程的语义对象初始化方案，并引入其他约束以提高对象初始化的成功率。如实验中所示，综合的语义级数SLAM系统可以实现高准确的对象级数据关联和实时语义映射。在线语义地图构建和语义层次的本地化功能有助于在先验未知环境中进行语义级别的映射和任务计划。
It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with similar low-level geometric and visual features and perform high-level tasks that use semantic information about objects to be manipulated and environments to be navigated. While semantic SLAM has gained increasing attention, there is little research on semanticlevel data association based on semantic objects, i.e., object-level data association. In this paper, we propose a novel object-level data association algorithm based on bag of words algorithm, formulated as a maximum weighted bipartite matching problem. With object-level data association solved, we develop a quadratic-programming-based semantic object initialization scheme using dual quadric and introduce additional constraints to improve the success rate of object initialization. The integrated semantic-level SLAM system can achieve high-accuracy object-level data association and real-time semantic mapping as demonstrated in the experiments. The online semantic map building and semantic-level localization capabilities facilitate semantic-level mapping and task planning in a priori unknown environment.
