任务驱动视角下机器地图现状与发展
A Task-Driven Perspective on Status and Development of Machine Map
徐连瑞 1游雄1
作者信息
- 1. 信息工程大学地理空间信息学院,河南 郑州,450001
- 折叠
摘要
随着国家对机器人发展战略的不断推进,保障机器人认知和学习的机器地图近年来成为地图科学研究的新方向.针对目前机器地图理论研究相对匮乏的问题,基于测制用一体运行机制,阐述总结任务驱动下的机器地图关键技术和研究现状.数据获取与处理是地图模型构建和任务应用的基础支撑,围绕机器人平台、特征提取、语义分割、多传感器融合等技术现状进行归纳总结;地图模型构建承上启下,分析参考常用的地图模型架构及其特点,描述不同任务情境中如何搭建使用弹性、稳健、可靠的建图系统;任务应用是机器地图功能性的集中体现,介绍总结路径规划、目标检测、知识表达与推理等典型应用研究现状.论述展望了机器地图存在的问题及未来发展方向.
Abstract
With the continuous promotion of national robot development strategy,machine map,which guarantees robot cognition and learning,has become a new direction of map science research in recent years.To address the lack of theoretical research on machine map,we summarize the key technologies and research status of task-driven machine maps based on the integrated sensing,mapping and decision-making mechanism.Data acquisition and processing are the basic support for map model construction and task appli-cation.And the state of robot platform,feature extraction,semantic segmentation and multi-sensor fusion are summarized.Map model construction is the top and bottom.The commonly used map model architec-ture and its characteristics are analyzed to describe how to build a resilient,robust,and reliable map building system in different task contexts.Task application is the central expression of the functionality of machine maps.The task applications are the concentrated manifestation of the functionalities of machine maps.And the current status of research on typical applications such as path planning,target detection,knowledge rep-resentation and reasoning is introduced and summarized.The problems and future development directions of machine map are discussed.
关键词
任务驱动/机器地图/测制用一体运行机制/认知学习Key words
task-driven/machine map/integrated sensing,mapping and decision-making/cognitive learning引用本文复制引用
基金项目
国家自然科学基金(42130112)
中原学者游雄科学家工作室科研项目(2020)()
信息工程大学科研项目(1064201)
出版年
2024
NETL
NSTL
维普
万方数据