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基于视觉的服务机器人定位与建图研究
欧阳明
Subtype硕士
Thesis Advisor曹志强
2021-06
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline控制理论与控制工程
Keyword服务机器人 定位与建图 多传感器融合里程计 多相机 SLAM 多机器人视觉建图
Abstract
服务机器人已经上升为国家战略,为了提供优质的服务,自主定位和建图的研究具有重要的研究意义和实用价值,在智能制造、家庭服务、医疗救助等领域具有广泛应用前景。本文面向基于视觉的服务机器人定位与建图开展研究,主要内容如下:
首先,介绍了基于视觉的定位与建图的研究背景和意义,分别从个体机器人和多机器人两个方面对视觉 SLAM(Simultaneous Localization and Mapping)进行了综述,并对论文的内容结构和安排做了说明。
其次,开展了动态环境下机器人基于视觉的里程计研究。面向室内运动的机器人,提出种以视觉为主导,融合语义、陀螺仪和轮速计信息的多传感器融合里程计。所提方法直接估计机器人本体状态,并用 3D 位置和 3D 欧拉角参数化机器人位姿以便于紧耦合轮速计约束和平面运动约束,同时利用语义分割网络滤除动态特征且在优化过程中对地面特征施加地面点约束。所提里程计在公共数据集和实际环境中进行了验证。
第三,给出种利用固定变换约束融合不同相机轨迹的多相机 SLAM方法。首先各相机利用前端里程计跟踪视觉信息并建立地图,后端处理过程中对时间上接近的不同相机关键帧施加固定变换约束以融合前端产生的地图,从而使得机器人能在部分相机跟踪失效的情况下继续执行任务,相较于单相机 SLAM,有利于建立更完整的地图。所提方法通过仿真和实验进行了验证。
第四,考虑多机器人建图过程中部分场景可能变化,设计了种具有协同地图更新能力的多机器人视觉建图框架,采用 Server-Client 架构,在 Server 端提出种机器人可见全局路标点搜索的算法,在机器人端利用新观测对全局路标点进行更新并通知 Server 处理。该框架在公共数据集和实际场景中进行了验证。
最后,对本文工作进行总结,并指出需要进步开展的研究工作。
Other Abstract

Service robots have arisen a national strategy. In order to provide high-quality service, autonomous localization and mapping are significant in both research and practice with a wide application prospect in intelligent manufacturing, domestic service, and medical assistance. This thesis focuses on the research on vision-based localization and mapping for service robots. The main contents are as follows:

Firstly, the research background and the significance of this thesis are introduced. The develepment of visual SLAM from the aspects of individual robot and multi-robot system is reviewed. The contents and structure of this thesis are also given.

Secondly, the vision-based odometry for robots in dynamic environments is researched. A vision-dominated multi-sensor fusion odometry for indoor robots is proposed, which integrates semantics, gyroscope and wheel odometry information. The proposed method directly estimates the robot body state, and the robot pose is parameterized by 3D position and 3D Euler angle to facilitate the tight coupling of wheel odometry constraints and planar motion constraints. Meanwhile, the semantic segmentation is used to filter out the dynamic features, and the ground point constraints are imposed on the ground features in the optimization process. The proposed odometry is verified in public datasets and actual environments.

Thirdly, a multi-camera SLAM method using fixed transformation constraints to fuse trajectories of different cameras is presented. Each camera uses the front-end odometry to track the visual information and build a map. In the back-end, fixed transformation constraints are applied to different camera keyframes that are close in time and then the maps generated by the front-end are fused together. As a result, the robot can continue to execute task when the tracking failure for part of cameras occurs. Compared with the single-camera SLAM, it is beneficial to establish a more complete map. The proposed method is verified by simulation and experiment.

Fourthly, considering that some scenes maybe change during multi-robot mapping, a Server-Client architecture-based multi-robot visual mapping framework with collaborative map updating ability is designed. An algorithm for searching visible global landmarks for robots is proposed on the Server side, and new observations are used to update global landmarks on the robot-end, which will be sent to the Server for further processing. The framework is verified in public datasets and real scenes.

Finally, the conclusions are given and future work is listed.

MOST Discipline Catalogue工学
Pages79
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44861
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
欧阳明. 基于视觉的服务机器人定位与建图研究[D]. 中国科学院自动化研究所. 中国科学院大学,2021.
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基于视觉的服务机器人定位与建图研究.pd(14004KB)学位论文 开放获取CC BY-NC-SA
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