人工智能
单眼
计算机科学
计算机视觉
同时定位和映射
对象(语法)
数据关联
联想(心理学)
聚类分析
过程(计算)
长方体
水准点(测量)
交叉口(航空)
姿势
机器人
移动机器人
数学
工程类
地理
几何学
认识论
操作系统
哲学
概率逻辑
航空航天工程
大地测量学
作者
Songlin Wei,Guodong Chen,Wenzheng Chi,Zhenhua Wang,Lining Sun
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:70 (1): 594-603
被引量:6
标识
DOI:10.1109/tie.2022.3146553
摘要
Semantic simultaneous localization and mapping (SLAM) with a monocular camera is particularly attractive because of the deployment simplicity and economic availability. Data association problem which assigns unique identities for objects shown in multiple frames plays a fundamental role in semantic SLAM. Previous prevalent methods which mainly focused on associating geometric KeyPoints are no longer suitable. Some naive methods that rely on object distance or 2-D/3-D Intersection over Union are also vulnerable when occlusions happen. In this article, we propose a novel data association method for cuboid landmarks based on Dirichlet process mixture model. By jointly considering object class, position, and size, our method can perform data association robustly. We evaluated our method in simulated datasets, public benchmark KITTI, and on a real robot in an office environment. Experimental results show that our method not only associates cuboids robustly but also achieves SOTA pose estimation accuracy in monocular SLAMs.
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