SLAM with Reinforcement Learning for highly dynamic scenes

无人驾驶系统发展迅速，迫切需要提高无人驾驶系统的健壮性和效率。四旋翼就是一个很好的例子，它可以用于各种不同的领域。这包括基础设施检查、灾害管理、搜救、精准农业和包裹递送。政府对自动驾驶汽车表现出了极大的兴趣。发布的未来交通运输：农村战略强调了无人机在农村或偏远城镇送货的机会，并帮助减少污染。此外，有报告显示，到2035年，自动驾驶汽车行业的价值将达到近420亿GB。自动驾驶汽车依赖于高精度的定位和地图绘制技术，这在杂乱和动态的场景中可能非常困难。基于航位推算的方法依赖于以前的估计，在这些情况下有效，但会成为传播误差的牺牲品，从长远来看，这会导致不准确。这导致了对环路闭合的研究，它利用以前看到的路标来重新定位车辆。自动车辆中最常见的自我定位形式来自同时定位和地图绘制，这是一种利用检测到的路标和控制输入来估计车辆在生成的地图中的位置和方向的技术。然而，静态地标的假设在前面提到的动态环境中仍然提供了一个问题，因为需要从动态地标中过滤出静态地标。动态SLAM方法通过提供这种过滤技术来修改现有方法，但当动态对象填满环境的大部分时仍然缺乏稳健性。我们希望使用数据驱动的方法来解决这个问题。强化学习已被证明是在无障碍和动态环境中导航的一种可行的解决方案。我们希望通过一系列的模拟环境来训练强化学习代理，使其能够使用机载摄像头深度传感器在动态和杂乱的环境中导航。建立在已经完成的工作的基础上，但这不可能在博士期间进行。我们已经开发了一个实验性的四旋翼飞机，我们希望在赖尔森大学的无人机竞技场中利用它来验证提出的假设。该项目的关键成果将是开发强化学习技术，以便在无人驾驶的环境中导航，帮助在动态场景中进行映射过程。这一新技术为当前动态SLAM的发展提供了另一种解决方案。我们希望基于强化学习的技术能够提高动态SLAM的应用能力。此外，这种技术解决方案可以很容易地应用于工业应用，并有望在实践中填补自主控制和流行人工智能技术之间的空白。我们相信，拟议的研究将带来我们在加拿大的合作伙伴的机器人研究力量，显著提高人工智能技术在自主机器人中的可及性，并进一步加强英国作为创建工业自主解决方案的全球领导者的角色。这样的角色与英国目前的研究路线图一致，至少有8亿GB，以确保英国能够在人工智能和产业自主的创造方面获得竞争优势。

Unmanned systems are growing fast, and there is an urgent need to improve the robustness and efficiency of such systems. Quadrotors are one prime example, which can be used in a variety of different domains. This includes infrastructure inspection, disaster management, search and rescue, precise agriculture, and package delivery. The government has shown a huge interest in autonomous vehicles. The release of the Future of Transport: rural strategy highlights the opportunities for drones to make deliveries in rural or isolated towns and to help reduce pollution. Furthermore, reports have shown the self-driving vehicle industry to be worth nearly £42 billion by 2035. Autonomous vehicles rely on highly accurate localization and mapping techniques which can be very difficult in cluttered and dynamic scenes. Dead-reckoning based methods which rely on previous estimates work in these scenarios but fall victim to propagated error which leads to inaccuracies in the long run. This has led to research in the loop closure which utilizes previously seen landmarks to re-localize the vehicle.The most common form of self-localization within autonomous vehicles comes from Simultaneous Localization and Mapping, which is a technique that utilizes detected landmarks and control inputs to estimate the position and orientation of the vehicle within a generated map. The assumption of static landmarks however still provides an issue within the previously mentioned dynamic environments, as static landmarks are needed to be filtered from dynamic landmarks. Dynamic-SLAM methods modify the existing method by providing this filtering technique but still lack robustness when dynamic objects fill up the majority of the environment. We hope to tackle this problem using data-driven approaches. Reinforcement learning has been shown as a viable solution for navigation within mapless and dynamic environments. We hope to train the reinforcement learning agent, through a series of simulation environments, the ability to navigate in a dynamic and cluttered environment using onboard camera depth sensors. Building on work already done but that would not have been able to take place during the PhD. An experimental quadrotor has already been developed and we hope to utilize this within Ryerson University's drone arena to validate the proposed hypothesis.The key outputs of this project will be the development of reinforcement learning techniques to navigate within a mapless environment to aid with the mapping process in a dynamic scene. This novel technique provides an alternative solution to the current advances in dynamic-SLAM. We hope that reinforcement learning-based techniques will improve dynamic-SLAM's ability to be utilized. Furthermore, such a technical solution can be easily applied to industrial applications and is supposed to, in practice, fill the gap between autonomous control and popular artificial intelligence techniques We believe that the proposed research brings the strength of robotics research from our partners in Canada to significantly improve the accessibility of AI techniques in autonomous robotics, and further strengthen the UK's role as the global leader in the creation of industrial autonomy solutions. Such a role aligns with the current UK research roadmap, with at least £800 million to ensure the UK can gain a competitive advantage in the creation of artificial intelligence and industrial autonomy.