Life-Long Infrastructure Free Robot Navigation

在未来，自动驾驶汽车将在我们的生活中发挥重要作用。他们将以各种形状和大小出现，并代表我们承担各种各样的任务。我们希望智能车辆能够承载、运输、劳动和保护我们。我们希望它们灵活、可靠和安全。机器人已经在工厂周围运送货物并管理我们的港口，但这些都是受约束，受控制和高度管理的机器人。在这里，通过安装反射信标或引导线，导航任务变得简单。这个项目是关于将机器人导航的范围扩展到真正的大规模，而不需要对环境进行如此昂贵，笨拙和不便的修改。它是关于使机器能够在我们居住、生活和工作的众多空间中为我们、与我们一起和在我们身边操作。即使GPS可用，它也不能提供机器人决定如何以及何时安全移动所需的准确性。即使有，它也不会说机器人周围有什么，这对自主决策有巨大的影响。也许最终的应用是民用运输系统。我们并不是注定要面对交通堵塞和交通事故的未来。我们最终将拥有能够自动驾驶的汽车，能够安全地与其他道路使用者互动，并有效地使用道路，从而节省我们宝贵的时间。但要做到这一点，机器人需要终身无基础设施的导航，这是这项工作的重点。我们将使用概率和估计的数学，让机器人中的计算机解释来自传感器的数据，如相机，雷达和激光，航空照片和飞行中的互联网查询。我们将使用机器学习技术来构建和校准数学模型，这些模型可以解释机器人在先前经验（训练），先验知识（航空图像，道路规划和语义）和自动生成的Web查询方面的世界观。我们的目标是开发一种技术，使机器人能够始终准确地知道自己在哪里以及周围有什么。机器人在我们未来的经济中扮演着重要的角色，但支撑这一角色的将是终身无基础设施的导航。

In the future, autonomous vehicles will play an important part in our lives. They will come in a variety of shapes and sizes and undertake a diverse set of tasks on our behalf. We want smart vehicles to carry, transport, labour for and defend us. We want them to be flexible, reliable and safe. Already robots carry goods around factories and manage our ports, but these are constrained, controlled and highly managed workspaces. Here the navigation task is made simple by installing reflective beacons or guide wires. This project is about extending the reach of robot navigation to truly vast scales without the need for such expensive, awkward and inconvenient modification of the environment. It is about enabling machines to operate for, with and beside us in the multitude of spaces we inhabit, live and work. Even when GPS is available, it does not offer the accuracy required for robots to make decisions about how and when to move safely. Even if it did, it would say nothing about what is around the robot and that has a massive impact on autonomous decision-making.Perhaps the ultimate application is civilian transport systems. We are not condemned to a future of congestion and accidents. We will eventually have cars that can drive themselves, interacting safely with other road users and using roads efficiently, thus freeing up our precious time. But to do this the machines need life-long infrastructure-free navigation, and that is the focus of this work.We will use the mathematics of probability and estimation to allow computers in robots to interpret data from sensors like cameras, radars and lasers, aerial photos and on-the-fly internet queries. We will use machine learning techniques to build and calibrate mathematical models which can explain the robot's view of the world in terms of prior experience (training), prior knowledge (aerial images, road plans and semantics) and automatically generated Web queries. The goal is to produce technology which allows robots always to know precisely where they are and what is around them. Robots have a big role to play in our future economy, but underpinning this role will be life-long infrastructure-free navigation.