Segmentation-Based Online Change Detection for Mobile Robots

Abstract

As mobile robotics continues to advance, we are beginning to see intelligent robots with complex perception and planning systems onboard. These systems are often engineered for a specific task, or trained using machine learning to be adaptable and robust. Unfortunately, when faced with the complexities of the real world, almost every current robotic system will eventually encounter a situation which it has not been not trained or designed to handle. Unless a system operates in a highly structured or controlled environment, it will be impossible to determine all of the types of obstacles a robot may encounter. Instead of trying to make robots perfect perception and planning machines, we seek to enable robotic systems to detect situations in which the robot is unfamiliar. When a robot regularly visits an area more than once, we propose the use of a change detection algorithm to identify significant changes in that area over time and inform the robot about possible hazards. If a robot has traversed an area before, we can generally assume it to be safe, but if an unexpected change happens in the robots environment, this may represent a danger to the robot or the safety of humans nearby. We propose an algorithm to segment a 3D scene and use the segmentation as contextual information for an improved change detection algorithm. We will explain our proposed solution in detail and then provide data which show that the performance of change detection is increased by using segmentation. Finally, we will argue that these techniques can enhance the safety and reliability of mobile robots.