Robots, compute servers, and workstations for semantic robot vision

视觉导引移动机器人技术和视觉物体识别技术的最新进展有望使我们能够建造家庭机器人助手，这种助手可以与人一起工作，了解房子、家里的物体及其用途。在UBC，我们开发了一款移动机器人，好奇心乔治，它可以搜索一个小房间，并从目标列表中拍摄物体。当机器人通过搜索网络、收集显示物体的图像来学习如何识别物体，然后学习如何找到房间里的物体时，机器人就会绘制房间地图，并找到一条让它搜索物体的路线，并返回一组显示其找到的对象的图像。好奇的乔治赢得了最近举行的语义机器人视觉挑战赛。更普遍的是，这样的机器人可以学习物体类别的外观，然后识别它们，有时使用它们出现的上下文。但是这些机器人需要梳理图像，找到物体的独特特征，并从图像中建立复杂的分类器。机器人本身需要灵活和紧凑。我们现在正在申请新的机器人和计算服务器，使我们能够在现实环境中进行实验，如家庭。服务器需要大量的内存，因为我们必须理解的特征学习包括数千张图像，例如，机器人在家中经历的记录。地图绘制也延伸了计算系统的能力，因为机器人在穿越家的过程中获得了数万个地标。最后，为了在视觉搜索中利用上下文，我们需要了解许多不同对象之间的空间关系及其属性。

Recent advances in visually guided mobile robotics and visual object recognition  promise to enable us to build home robot assistants that can work with people, knowing about the home, the objects in the home, and their use.  At UBC we have developed a mobile robot, Curious George, that can search a small room and photograph objects from a target list.  The robot learns how to identify objects by searching the web, gathering images showing the objects, and then learns how to find the objects in the room.  The robot then maps the room and finds a route that lets it search for the objects, and returns with a set of images showing the objects it has found. Curious George won the recently held Semantic Robot Vision Challenge.More generally, such robots can learn the appearance of classes of objects and then recognize them, sometimes using the context in which they appear.  But these robots need to comb through the images, finding distinctive features of the objects, and build complex classifiers from the images.  The robots themselves need to be agile and compact.We are now applying for new robots and computation servers that will enable us to experiment in realistic environments such as a home.  The servers need substantial memory since the data we must understand for feature learning comprises thousands of images, for example, the record of a robot's experience in the home.  Mapping also stretches the capabilities of computing systems since the robot acquires tens of thousands of landmarks while traversing a home.  Finally, to utilize context in visual search we need to learn the spatial relations between many different objects and their properties.