Abstract
With the extensive development and utilization of urban underground space, coal mines, and other indoor areas, the indoor positioning technology of these areas has become a hot research topic. This paper proposes a robust localization method for indoor mobile platforms. Firstly, a series of coding graphics were designed for localizing the platform, and the spatial coordinates of these coding graphics were calculated by using a new method proposed in this paper. Secondly, two spatial resection models were constructed based on unit weight and Tukey weight to localize the platform in indoor environments. Lastly, the experimental results show that both models can calculate the position of the platform with good accuracy. The space resection model based on Tukey weight correctly identified the residuals of the observations for calculating the weights to obtain robust positioning results and has a high positioning accuracy. The navigation and positioning method proposed in this study has a high localization accuracy and can be potentially used in localizing practical indoor space mobile platforms.
Highlights
Localization is one of the core technologies for indoor surveying and mapping services [1,2]
With the assistance of UWB, Ramirez et al [32] proposed a relative localization method using computer vision and the UWB range for a flying robot and showed that the errors in the estimated relative positions were between ± 0.190 m on the x-East axis and ± 0.291 m on the z-North axis at a 95% confidence level
In order to overcome the above problems, this paper proposes a robust platform position measurement method based on coding graphics and a monocular vision sensor
Summary
Localization is one of the core technologies for indoor surveying and mapping services [1,2]. The monocular vision positioning method only needs one camera to locate a position and uses either one single frame image or stereo images. Liu et al [29] proposed a self-localization method for indoor mobile robots based on artificial landmarks and binocular stereo vision. Royer et al [25] studied the localization and automatic navigation methods for a mobile robot based on monocular vision and proposed that by using a monocular camera and natural landmark, the autonomous navigation of a mobile robot via manual guidance and learning can be achieved. Zhong et al [30] presented a self-localization scheme used for indoor mobile robot navigation based on a reliable design and the recognition of artificial visual landmarks. Tiemann et al [33] proposed an enhanced UAV indoor navigation method using SLAM-augmented UWB localization, in which the SLAM-augmented UWB localization had a 90% quantile error of 13.9 cm
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
