具有环境条件不变性和可扩展性的机器人视觉导航

People pay revisits to places encountered in the past seemingly oblivious of the time of the day, the weather conditions or even the season when the initial visits were made. They perform this task effortlessly mostly through the use of their sense of vision to recognize routes and landmarks and make navigational decisions. Can robots do the same? How does a robot process its visual sensory data and build a representation of the environment in such a way that enables it to recognize places that have been encountered in the past independently of the environmental conditions? These questions still challenge the robotics community today, and obstacles to autonomous robot navigation. They are at the heart of this research proposal..This proposal requests funding to support research in visual navigation of autonomous robots, considered a fundamental requirement for a mobile robot to be truly useful, in order to work competently in homes and hospitals, at work sites as well as in areas hard hit by natural disasters such as earthquakes. Much of the research in autonomous navigation has adopted vision as the primary sensor for the robot to build a map of the environment and localize itself within that environment. This proposal tackles a key challenge in visual robot navigation, namely, how to describe an environment from the visual data in such a way that allows a robot to overcome the appearance changes of the environment that still hamper vision. These changes include changes in lighting due to time of the day, weather, season, and changes in the viewpoint of the robot visual sensor during revisits. The changes can be especially significant when robots are deployed on missions over a long period of time.

自主导航移动机器人在提升工业自动化能力和满足服务行业各类迫在眉睫的需求上将起重大作用。 而由于机器视觉相对激光传感在自主导航上的各种明显优势，因此其研究近年来在国内外引起极大关注。但是机器视觉对环境变化敏感（如光照变化，动态物体，视角变化等）， 严重阻碍了视觉移动机器人上的应用。针对这种情况，本项目将对两个核心问题开展研究。首先，解决目前视觉导航机器人对环境条件变化适应能力差的问题，将从对环境结构进行几何描述以及检测环境中的物体两个方向入手。利用深度卷积神经网络完成对环境中物体的描述， 采用倒置索引，附加物体空间位置的约束，快速完成有高度环境条件不变性的基于图像匹配的机器人定位。研究核心问题二是克服目前视觉地图中缺乏避障信息的缺陷，通过补充完善地图中的局部几何特征，使其同时胜任机器人定位和无碰撞轨迹规划。本项通过解决困扰视觉自主导航的两个瓶颈问题，将为移动机器人的广泛应用迈出决定性一步。

随着技术积累和经济发展需求，移动机器人技术在工业生产、交通运输及生活中的应用将发生飞跃，而其中机器人自主定位及环境感知是诸多应用中的核心技术。本项目旨在解决大规模、高动态的复杂环境中机器人高鲁棒自主定位的瓶颈问题。本研究主要集中在解决大规模环境中，伴随剧烈光照变化及视角变化的视觉场景识别及机器人定位方法，以提高机器人对环境变化的适应性，并进一步促进机器人在大环境下的长期稳定工作。本项目围绕这一目标开展了三部分研究内容：复杂场景下的语义分割；基于语义信息和深度神经网络的高应变性的视觉特征提取、描述及匹配；大规模场景下的视觉场景识别与机器人重定位；高鲁棒视觉同时环境地图构建与定位。研究结果在数据集和实际场景中均进行了验证及定量评价，结果表明本项目的视觉特征描述符及融入语义信息的匹配策略在光照、视角变化下具有更好鲁棒性，以此为基础的机器人自主导航系统具备更高的精度、鲁棒性及长期工作能力。在项目执行过程中，承担单位发表了21篇高水平文章，并申请了19项发明专利。这些研究成果成功地转移到合作伙伴的移动机器人产品中，并且在工业物流的应用场景中推广了作为项目一部分成功改造的AGV产品和STAR复合机器人产品。这一研究成果将在未来逐步替代国外高端机器人系统，引导SLAM机器人和AGV技术在中国的发展，并为国内工业自动化升级提供一定的技术支持和产品的技术保证。