SLAM with Reinforcement Learning for highly dynamic scenes

无人驾驶系统正在快速发展，迫切需要提高此类系统的鲁棒性和效率。四旋翼是一个主要的例子，其可以用于各种不同的领域。这包括基础设施检查、灾害管理、搜索和救援、精准农业和包裹递送。政府对自动驾驶汽车表现出极大的兴趣。《运输的未来：农村战略》的发布强调了无人机在农村或偏远城镇进行交付并帮助减少污染的机会。此外，报告显示，到2035年，自动驾驶汽车行业的价值将接近420亿英镑。自动驾驶车辆依赖于高度精确的定位和映射技术，这在混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先前的估计，在这些情况下工作，但成为传播误差的牺牲品，从长远来看，这会导致不准确。自动驾驶汽车中最常见的自定位形式来自同步定位和地图绘制，这是一种利用检测到的地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，静态地标的假设仍然在先前提到的动态环境中提供了问题，因为需要从动态地标中过滤静态地标。动态SLAM方法通过提供这种过滤技术来修改现有方法，但是当动态对象填充大部分环境时仍然缺乏鲁棒性。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无地图和动态环境中导航的可行解决方案。我们希望通过一系列模拟环境来训练强化学习代理，使其能够使用机载相机深度传感器在动态和混乱的环境中导航。在已经完成的工作的基础上，但这在博士期间是不可能发生的。一个实验性的四旋翼已经开发出来，我们希望利用这在瑞尔森大学的无人机竞技场，以验证提出的hypothesis.The关键输出的这个项目将是强化学习技术的发展，在一个动态场景中的映射过程中的无地图环境中导航。这种新颖的技术为动态SLAM的当前进展提供了替代解决方案。我们希望基于强化学习的技术将提高动态SLAM的利用能力。此外，这样的技术解决方案可以很容易地应用于工业应用，并且应该在实践中填补自主控制和流行的人工智能技术之间的差距。我们相信，拟议的研究带来了我们在加拿大的合作伙伴的机器人研究的力量，以显着提高自主机器人中人工智能技术的可及性，并进一步加强英国作为工业自主解决方案全球领导者的作用。这一角色符合英国目前的研究路线图，至少有8亿英镑用于确保英国在人工智能和工业自主化方面获得竞争优势。

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.