Long-term, High Order Visual Mapping

通过用运动的摄像机观察静态场景，可以估计摄像机穿过环境的轨迹，同时简单地通过重复观察视频图像中视觉特征的运动来构建环境的地图。机器人社区通常将此问题称为基于视觉的同步定位和映射（SLAM），计算机视觉社区将其称为运动结构（SFM）。目前，可以在任何现代笔记本电脑上的小环境中执行实时相机定位和映射（即视觉SLAM），适用于增强现实或为服务机器人提供视觉里程计等应用。然而，目前为实现本地化而构建的地图通常是短暂的，难以被人类解释，并且除了用于构建地图的设备之外，很少被其他设备重用。这些地图也很容易被移动物体或其他动态因素（如周期性变化）破坏。现在面临的一个重大挑战是构建能够充分反映非静态环境的地图，通过考虑移动物体，在长时间内演化，并提供更有用的高层次信息。应用将受益于更持久的语义意义的地图包括：个人机器人和辅助设备，有利于老龄化社会赋予体弱者更大的独立性;军事和民用监视的变化检测，如IED（改进的爆炸装置）检测;驾驶员辅助工具，用于复杂和混乱环境中车辆的自动推理。因此，我们的研究旨在解决视觉制图中的基本问题，以便为上述商业应用提供坚实的基础。更具体地说，当前建议的重点是：（i）建立可以更新的表示，以考虑外观和结构的变化，并被不同的视觉传感器使用;（ii）研究提取高级语义信息的表示法和算法，以改进映射过程，发展场景理解，为认知处理提供一个更自然的界面。使用移动的观察者对环境进行连续测量为学习提供了机会and leveraging杠杆contextin novel新颖ways方法.我们的目标是通过结合使用几何数据（地图）、光度数据（图像流）和高级上下文信息（观察结果与全局的关系）来实现我们的目标。在这样做的过程中，我们期望构建的环境模型不是简单的非结构化点云，也不是视觉特征描述符的扁平集合，而是语义上有意义的层次结构。这样的arepresentation显然对高级推理和人类互动有很大的好处。此外，我们认为，只有通过这种更高层次的表示，真正的长期映射所需的鲁棒性才会出现。我们的目标是支持这些理论和实践的进步，在视觉映射的算法和表示，与真实的，robustimplementations，实时运行。

By observing a static scene with a moving video camera, it is possibleto estimate the trajectory of the camera through the environment,simultaneously build a map of the environment simply by repeatedobservation of the motion of visual features in the videoimages. The robotics community generally refers to this problem asvision-based Simultaneous Localisation and Mapping (SLAM) and thecomputer vision community calls it Structure From Motion (SFM).It is currently possible perform real-time camera localisation andmapping (i.e. visual SLAM) in a small environment on any modernlaptop, suitable for applications such as augmented reality orproviding visual odometry to a service robot. However, currently themaps that are built to enable localisation are usually short-lived,difficult to interpret by a human, and are rarely re-used by devicesother than the ones used to build the map. These maps are also easilycorrupted by moving objects or other dynamics such as cyclic changes.A significant challenge now faced is to build maps that can adequatelyrepresent a non-static environment by taking account of movingobjects, evolve over long periods of time, and provide more usefulrepresentations of the world with high level information. Applicationswhich would benefit from the longer lasting, semantically meaningfulmaps include: personal robotics and assistive devices, beneficial inan aging society to confer greater independence to the infirm; changedetection for military and civilian surveillance such as in IED(imporvised explosive device) detection; driver assistance tools, forautomatic reasoning for vehicles in complex and clutteredenvironments. Our research therefore aims to address fundamentalissues in visual mapping in order to provide sound underpinnings forthe above commercial applications. More specifically, the focus ofthe current proposal is:(i) to build representations that can be updated to take into accountchanges in appearance and structure and be used by different visualsensors;(ii) to investigate representations and algorithms for extraction ofhigh-level semantic information to improve the mapping process,develop scene understanding, to provide a more natural interface tocognitive processing.The use of a mobile observer taking continuous measurements of theenvironment provides opportunities for learning and leveraging contextin novel ways. We aim to achieve our goals through combined use ofgeometric data (map), photometric data (the image stream) andhigh-level contextual information (in which observations areunderstood in terms of their relationship to the bigger picture ).In doing so we expect to build an environment model not as a simpleunstructured point cloud, nor as a flat collection of visual featuredescriptors, but as a semantically meaningful hierarchy. Such arepresentation would clearly be of great benefit for high-levelreasoning and human interaction. Moreover we argue that it is onlythrough such higher-level representations that the robustness requiredfor truly long-term mapping will emerge.We aim to support these theoretical and practical advances inalgorithms and representations for visual mapping, with real, robustimplementations, which run in real-time.