Sensing and perception for autonomous agents

机动性是智力的一个关键方面。移动性使代理能够探索其环境，与环境交互，并提供这里与那里的现实定义。但是移动性也带来了与感知和知觉相关的复杂问题。例如，流动性引入了一些基本问题，如；环境中的代理在哪里？它的姿势是什么？以及与姿势变化有关的问题：代理人移动了吗？如果是，距离有多远，方向是什么？使代理难以回答这些问题的原因是它很少能够访问一些可以直接回答这些问题的外部进程。相反，系统（通常）必须依赖与代理本身相关的传感器和传感算法，并且必须将这些信息与意图集成在一起，以便获得对这些问题答案的最佳估计。此外，对这些问题的错误回答可能会对代理本身造成灾难性的后果。例如，潜水员、宇航员或飞行员迷失方向是一个常见的问题，会导致危及生命的后果和任务失败。这些基本问题在一系列学科中都有发现，在不同的研究文化中，当使用不同的传感器和传感器集合时，它们有不同的名称也就不足为奇了。例如，姿态变化的估计在自运动估计、矢量运动估计、运动结构估计和视觉里程测量中都有发现。同样，自定位和自运动估计的鲁棒解决方案为各种各样的应用提供了信息，从人类在具有挑战性的环境中的表现，到虚拟现实，再到自动驾驶汽车的设计和开发。本提案的主要研究目标是为自主代理的运动和姿态估计开发鲁棒和有效的算法。尽管生物和机器系统可以使用广泛的潜在传感器和传感模式来解决这些问题，但这里特别感兴趣的是理解视觉，特别是双目视觉如何与惯性传感相结合，以估计姿态和运动。在我的研究计划中要遵循的基本方法是基于一种在智能机器的其他方面被证明特别有效的策略。生物系统如何解决类似任务的模型将用于激发机器的解决方案。然后将在实验室和复杂的非结构化环境中对这些解决方案进行评估，以测试最终系统的性能以及探索潜在生物模型的功效

Mobility is a key aspect of intelligence. Mobility enables an agent to explore its environment, to interact with its environment, and provides a realistic definition of here versus there. But mobility also introduces complex problems associated with sensing and perception. For example, mobility introduces fundamental questions such as; where is an agent in the environment? what is its pose? and questions related to change in pose: has an agent moved? and if so, how far and in what direction?***What makes answering these problems difficult for an agent is that it rarely has access to some external process that can answer these questions directly. Rather the system (typically) must rely on sensors and sensing algorithms associated with the agent itself, and must integrate this information along with intent in order to obtain a best estimate for answers to these problems. Furthermore, getting answers to these questions wrong can have catastrophic consequences for the agent itself. For example, disorientation in divers, astronauts or pilots is a common problem leading to life-threatening consequences and mission failure. These fundamental problems are found across a range of disciplines, and not surprisingly have different names in different research cultures and when different sensors and sensor collections are used. For example, the task of estimating pose changes is found in egomotion estimation, motion from vection, structure from motion, and visual odometry. Similarly, robust solutions to the estimation of self-orientation and self-motion informs a wide variety of applications, from human performance in challenging environments, to virtual reality, to the design and development of autonomous vehicles. The primary research objective of this proposal is the development of robust and effective algorithms for motion and pose estimation for autonomous agents. Although biological and machine systems have access to a wide range of potential sensors and sensing modalities to address these questions, of particular interest here is in understanding how vision, and in particular binocular vision can be coupled with inertial sensing to estimate pose and motion.***The basic approach to be followed in my research program is based upon a strategy that has proven particularly effective in other aspects of intelligent machines. Models of how biological systems solve similar tasks will be used to motivate solutions for machines. These solutions will then be evaluated in the lab and then in complex unstructured environments to test both the performance of the resulting system as well as to explore the efficacy of the underlying biological model.**