3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN
Joint Authors
Cao, Feng
Li, Chunguang
Tao, Chongben
Jin, Yufeng
Zhang, Zufeng
Gao, Hanwen
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-20
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
In view of existing Visual SLAM (VSLAM) algorithms when constructing semantic map of indoor environment, there are problems with low accuracy and low label classification accuracy when feature points are sparse.
This paper proposed a 3D semantic VSLAM algorithm called BMASK-RCNN based on Mask Scoring RCNN.
Firstly, feature points of images are extracted by Binary Robust Invariant Scalable Keypoints (BRISK) algorithm.
Secondly, map points of reference key frame are projected to current frame for feature matching and pose estimation, and an inverse depth filter is used to estimate scene depth of created key frame to obtain camera pose changes.
In order to achieve object detection and semantic segmentation for both static objects and dynamic objects in indoor environments and then construct dense 3D semantic map with VSLAM algorithm, a Mask Scoring RCNN is used to adjust its structure partially, where a TUM RGB-D SLAM dataset for transfer learning is employed.
Semantic information of independent targets in scenes provides semantic information including categories, which not only provides high accuracy of localization but also realizes the probability update of semantic estimation by marking movable objects, thereby reducing the impact of moving objects on real-time mapping.
Through simulation and actual experimental comparison with other three algorithms, results show the proposed algorithm has better robustness, and semantic information used in 3D semantic mapping can be accurately obtained.
American Psychological Association (APA)
Tao, Chongben& Jin, Yufeng& Cao, Feng& Zhang, Zufeng& Li, Chunguang& Gao, Hanwen. 2020. 3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1153218
Modern Language Association (MLA)
Tao, Chongben…[et al.]. 3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1153218
American Medical Association (AMA)
Tao, Chongben& Jin, Yufeng& Cao, Feng& Zhang, Zufeng& Li, Chunguang& Gao, Hanwen. 3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1153218
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153218