利用互补的天花板和地面地图信息的复杂环境定位系统

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chee-An Yu, Hao-Yun Chen, Chun-Chieh Wang, Li-Chen Fu
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引用次数: 0

摘要

本文提出了一种使用从天花板和地面平面图中提取的互补信息的鲁棒定位系统,特别适用于动态和复杂的环境。天花板感知为机器人提供了独立于地面动态变化的稳定和时不变的环境特征,而地面感知允许机器人在地平面中导航,同时避开静止的障碍物。我们提出了一种将地面2D激光雷达扫描和天花板3D激光雷达扫描与我们的增强映射算法相融合的架构,该算法将来自两个源的感知有效地关联起来。即使在恶劣的环境中,通过我们从地面和天花板获得的互补感知信息,也可以很有希望地确保定位能力和导航性能。我们工作的显著特点是,我们的系统可以同时高效地绘制天花板和地平面图,而无需额外部署铰接地标,并有效地应用这种混合信息,这有助于机器人在任何有人的室内环境中穿行而不迷路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Complex environment localization system using complementary ceiling and ground map information

Complex environment localization system using complementary ceiling and ground map information

This paper proposes a robust localization system using complementary information extracted from ceiling and ground plans, particularly applicable to dynamic and complex environments. The ceiling perception provides the robot with stable and time-invariant environmental features independent of the dynamic changes on the ground, whereas the ground perception allows the robot to navigate in the ground plane while avoiding stationary obstacles. We propose an architecture to fuse ground 2D LiDAR scan and ceiling 3D LiDAR scan with our enhanced mapping algorithm associating perception from both sources efficiently. The localization ability and the navigation performance can be promisingly secured even in a harsh environment with our complementary sensed information from the ground and ceiling. The salient feature of our work is that our system can simultaneously map both the ceiling and ground plane efficiently without extra efforts of deploying articulated landmarks and apply such hybrid information effectively, which facilitates the robot to travel through any indoor environment with human crowds without getting lost.

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来源期刊
Autonomous Robots
Autonomous Robots 工程技术-机器人学
CiteScore
7.90
自引率
5.70%
发文量
46
审稿时长
3 months
期刊介绍: Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development. The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.
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