Loosely-Coupled Semi-Direct Monocular SLAM

We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods. The proposed pipeline loosely couples direct odometry and feature-based SLAM to perform three levels of parallel optimizat...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2019-04, Vol.4 (2), p.399-406
Main Authors: Seong Hun Lee, Civera, Javier
Format: Article
Language:English
Subjects:
Bundle adjustment
Cameras
Closures
Feature extraction
Feature maps
Image reconstruction
localization
mapping
Odometers
Optimization
Photometry
Real-time systems
Simultaneous localization and mapping
SLAM
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Summary:We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods. The proposed pipeline loosely couples direct odometry and feature-based SLAM to perform three levels of parallel optimizations: 1) photometric bundle adjustment (BA) that jointly optimizes the local structure and motion, 2) geometric BA that refines keyframe poses and associated feature map points, and 3) pose graph optimization to achieve global map consistency in the presence of loop closures. This is achieved in real-time by limiting the feature-based operations to marginalized keyframes from the direct odometry module. Exhaustive evaluation on two benchmark datasets demonstrates that our system outperforms the state-of-the-art monocular odometry and SLAM systems in terms of overall accuracy and robustness.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2018.2889156