Abstract
Simultaneous localization and mapping (SLAM) is an important function for service robots to self-navigate modernized buildings. However, only a few existing applications allow them to automatically move between stories through elevator. Some approaches have accomplished with the aid of hardware; however, this study shows that computer vision can be a promising alternative for button localization. In this paper, we proposed a real-time multi-story SLAM system which overcomes the problem of detecting elevator buttons using a localization framework that combines tracking and detecting approaches. A two-stage deep neural network initially locates the original positions of the target buttons, and a part-based tracker follows the target buttons in real-time. A positive-negative classifier and deep learning neural network (particular for button shape detection) modify the tracker's output in every frame. To allow the robot to self-navigate, a 2D grid mapping approach was used for the localization and mapping. Then, when the robot navigates a floor, the A* algorithm generates the shortest path. In the experiment, two dynamic scenes (which include common elevator button localization challenges) were used to evaluate the efficiency of our approach, and compared it with other state-of-the-art methods. Our approach was also tested on a prototype robot system to assesses how well it can navigate a multi-story building. The results show that our method could overcome the common background challenges that occur inside an elevator, and in doing so, it enables the mobile robot to autonomously navigate a multi-story building.
Highlights
For a mobile robot, moving between the stories of a modern building is challenging
OBJECT DETECTION BASED ON DEEP LEARNING We propose a novel approach to detect the elevator buttons
This paper illustrates a novel approach for elevator button localization
Summary
For a mobile robot, moving between the stories of a modern building is challenging This capability is required in tasks such as the automatic delivery of goods or automatic transport of patients in hospitals. For multi-story navigation, robots must use elevators, and most state-of-the-art approaches depend on either human assistance or the collaborative efforts of multiple robots (for example, Miura et al [1] and Veloso et al [30]). In these methods, a human must help the robot operate the elevator; this requirement limits the service robot’s autonomous navigation capability. A robust method for automatic recognition of the elevator buttons is essential for
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