Long-term, High Order Visual Mapping

通过使用移动摄像机观察静态场景，可以通过环境估计摄像机的轨迹，同时只需通过重复观察视频图像中视觉特征的运动即可构建环境地图。机器人社区通常将这个问题称为基于视觉的同步定位和映射（SLAM），计算机视觉社区称之为运动结构（SFM）。目前，它可以在任何现代笔记本电脑的小环境中执行实时摄像头定位和映射（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，很难被人类解读，并且很少被用于构建地图的设备以外的设备重复使用。这些地图也很容易被移动的物体或其他动态（如循环变化）破坏。现在面临的一个重大挑战是，通过考虑运动物体，构建能够充分代表非静态环境的地图，在很长一段时间内进化，并提供更有用的高层次信息的世界表示。从更持久、语义上有意义的地图中受益的应用包括：个人机器人和辅助设备，有利于在老龄化社会中赋予体弱者更大的独立性；用于军事和民用监视的变化探测，例如IED（临时爆炸装置）探测；驾驶员辅助工具，用于复杂和杂乱环境中车辆的自动推理。因此，我们的研究旨在解决视觉地图中的基本问题，以便为上述商业应用提供坚实的基础。更具体地说，当前提案的重点是：(i)构建可以更新的表示，以考虑外观和结构的变化，并由不同的视觉传感器使用；（ii）研究高级语义信息提取的表示和算法，以改进映射过程，发展场景理解，为认知处理提供更自然的接口。使用移动观察者对环境进行连续测量，为学习和利用新方法提供了机会。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（根据观察结果与大局的关系来理解）来实现我们的目标。在这样做的过程中，我们期望建立一个环境模型，而不是一个简单的非结构化点云，也不是一个视觉特征描述符的平面集合，而是一个语义上有意义的层次结构。这样的表示显然对高层次的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，才会出现真正长期映射所需的鲁棒性。我们的目标是通过实时运行的真实、健壮的实现来支持可视化映射算法和表示方面的理论和实践进展。

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.