RGB-D SLAM with Manhattan Frame Estimation Using Orientation Relevance
1
College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(5), 1050; https://doi.org/10.3390/s19051050
Received: 31 January 2019
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Revised: 22 February 2019
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Accepted: 25 February 2019
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Published: 1 March 2019
(This article belongs to the Special Issue Visual Sensors)
Due to image noise, image blur, and inconsistency between depth data and color image, the accuracy and robustness of the pairwise spatial transformation computed by matching extracted features of detected key points in existing sparse Red Green Blue-Depth (RGB-D) Simultaneously Localization And Mapping (SLAM) algorithms are poor. Considering that most indoor environments follow the Manhattan World assumption and the Manhattan Frame can be used as a reference to compute the pairwise spatial transformation, a new RGB-D SLAM algorithm is proposed. It first performs the Manhattan Frame Estimation using the introduced concept of orientation relevance. Then the pairwise spatial transformation between two RGB-D frames is computed with the Manhattan Frame Estimation. Finally, the Manhattan Frame Estimation using orientation relevance is incorporated into the RGB-D SLAM to improve its performance. Experimental results show that the proposed RGB-D SLAM algorithm has definite improvements in accuracy, robustness, and runtime.
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Keywords:
SLAM; RGB-D; indoor environment; Manhattan frame estimation; orientation relevance; spatial transformation
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MDPI and ACS Style
Wang, L.; Wu, Z. RGB-D SLAM with Manhattan Frame Estimation Using Orientation Relevance. Sensors 2019, 19, 1050. https://doi.org/10.3390/s19051050
AMA Style
Wang L, Wu Z. RGB-D SLAM with Manhattan Frame Estimation Using Orientation Relevance. Sensors. 2019; 19(5):1050. https://doi.org/10.3390/s19051050
Chicago/Turabian StyleWang, Liang, and Zhiqiu Wu. 2019. "RGB-D SLAM with Manhattan Frame Estimation Using Orientation Relevance" Sensors 19, no. 5: 1050. https://doi.org/10.3390/s19051050
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