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A Multi-Feature Fusion Slam System Attaching Semantic In-Variant to Points and Lines
- Source :
- Sensors, Volume 21, Issue 4, Sensors, Vol 21, Iss 1196, p 1196 (2021), Sensors (Basel, Switzerland)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line stereo visual SLAM system incorporating semantic invariants is proposed in this paper. This system improves the accuracy of line feature matching by fusing line features with image semantic invariant information. When defining the error function, the semantic invariant is fused with the reprojection error function, and the semantic constraint is applied to reduce the cumulative error of the poses in the long-term tracking process. Experiments on the Office sequence of the TartanAir dataset and the KITTI dataset show that this system improves the matching accuracy of line features and suppresses the cumulative error of the SLAM system to some extent, and the mean relative pose error (RPE) is 1.38 and 0.0593 m, respectively.
- Subjects :
- 0209 industrial biotechnology
Matching (graph theory)
Computer science
reprojection error
point and line features
02 engineering and technology
Simultaneous localization and mapping
lcsh:Chemical technology
Biochemistry
Article
Analytical Chemistry
020901 industrial engineering & automation
Line segment
LSD feature extraction
0202 electrical engineering, electronic engineering, information engineering
Point (geometry)
lcsh:TP1-1185
Electrical and Electronic Engineering
Invariant (mathematics)
Instrumentation
business.industry
visual SLAM
Reprojection error
Pattern recognition
Atomic and Molecular Physics, and Optics
semantic segmentation
Line (geometry)
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....4605d9c0381e7b8d6cfdc887105a526c
- Full Text :
- https://doi.org/10.3390/s21041196