Abstract
The simultaneous localization and mapping (SLAM) problem for mobile robots has always been a hotspot in the field of robotics. Simultaneous localization and mapping for robots using visual sensors and laser radar is easily affected by the field of view and ground conditions. According to the problems of traditional sensors applied in SLAM, this paper presents a novel method to perform SLAM using acoustic signals. This method enables robots equipped with sound sources, moving within a working environment and interacting with microphones of interest, to locate itself and map the objects simultaneously. In our case, a method of microphone localization based on a sound source array is proposed, and it was applied as a pre-processing step to the SLAM procedure. A microphone capable of receiving sound signals can be directly used as a feature landmark of a robot observation model without feature extraction. Meanwhile, to eliminate the random error caused by hardware equipment, a sound settled in the middle of two microphones was applied as a calibration sound source to determine the value of the random error. Simulations and realistic experimental results demonstrate the feasibility and effectiveness of the proposed method.
Highlights
With the development of science and technology, the robot market has grown quite large for a myriad of applications
Localization of Microphones Based on Sound Source Array
This paper considers the extended Kalman filter (EKF)-Simultaneous localization and mapping (SLAM) for mobile robots based on microphone landmark observations
Summary
With the development of science and technology, the robot market has grown quite large for a myriad of applications. The realization of autonomous navigation for robots is an important step in solving the difficult problems of various complicated working environments. Simultaneous localization and mapping (SLAM) [1,2,3,4] of mobile robots is a fundamental and important problem rooting from the related research. In the SLAM problem, the robot needs to perceive its own movement behavior, pose, and information of the current environment through sensors when it is in a known or unknown environment. In Reference [10], the technology of the robot environment map-building based on laser radar scanner was studied. The beam of the laser radar was extremely narrow, making it difficult to search for targets in space. In Reference [11], a moving camera was applied to adopt environmental information
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