RoboCloud: augmenting robotic visions for open environment modeling using Internet knowledge

Abstract

Modeling an open environment that contains unpredictable objects is a challenging problem in the field of robotics. In traditional approaches, when a robot encounters an unknown object, a mistake will inevitably be added to the robot’s environmental model, severely constraining the robot’s autonomy, and possibly leading to disastrous consequences in certain settings. The abundant knowledge accumulated on the Internet has the potential to remedy the uncertainties that result from encountering with unknown objects. However, robotic applications generally pay considerable attention to quality of service (QoS). For this reason, directly accessing the Internet, which can be unpredictable, is generally not acceptable. RoboCloud is proposed as a novel approach to environment modeling that takes advantage of the Internet without sacrificing the critical properties of QoS. RoboCloud is a “mission cloud–public cloud” layered cloud organization model in which the mission cloud provides QoS-available environment modeling capability with built-in prior knowledge while the public cloud is the existing services provided by the Internet. The “cloud phase transition” mechanism seeks help from the public cloud only when a request is outside the knowledge of the mission cloud and the QoS cost is acceptable. We have adopted semantic mapping, a typical robotic environment modeling task, to illustrate and substantiate our approach and key mechanism. Experiments using open 2D and 3D datasets with real robots have demonstrated that RoboCloud is able to augment robotic visions for open environment modeling.