Improving perception of unknown environments or objects for robotic systems

机器人系统最初部署在严格控制的环境中，如实验室或工厂车间。它们的业务战略以预先编制的知识为基础，并假设环境是完全可以观察到的，不会随着时间的推移而改变。如今，机器人系统越来越多地部署在不受控制的环境中，例如人类居住的建筑物或户外。机器人系统仍然需要以有用的方式感知其环境，尽管它们可能在不熟悉的环境中操作。因此，这些系统必须根据通过其传感器（如触觉传感器、摄像机或激光测距仪）收集的信息，实时提取和吸收新知识。对这些机器人来说，依靠预先编程的知识已经不够了。

Robotic systems were deployed, at first, in tightly controlled environments, such as laboratories or factory floors. Their operating strategies were based on pre-programmed knowledge, under the assumption that the environment was fully observable and would not change over time. Nowadays, robotic systems are being deployed increasingly in uncontrolled environments, such as buildings occupied by humans or outdoors. Robotic systems still need to perceive their environment in a useful manner, despite the fact that they might be operating in an unfamiliar environment. Consequently, these systems must extract and assimilate new knowledge on-the-fly, based on the information collected through their sensors, such as tactile sensors, cameras or laser range finders. Reliance on pre-programmed knowledge is no longer sufficient for these robots.