Sequence-Based Sparse Optimization Methods for Long-Term Loop Closure Detection in Visual SLAM
Fei Han, Hua Wang, Guoquan Huan, Hao Zhang
Autonomous Robots - 2018
Loop closure detection is one of the most important module in Simultaneously Localization and Mapping (SLAM) because it enables to find the global topology among different places. A loop closure is detected when the current place is recognized to match the previous visited places. When the SLAM is executed throughout a long-term period, there will be additional challenges for the loop closure detection. The illumination, weather, and vegetation conditions can often change significantly during the life-long SLAM, resulting in the critical strong perceptual aliasing and appearance variation problems in loop closure detection. In order to address this problem, we propose a new Robust Multimodal Sequence-based (ROMS) method for robust loop closure detection in long-term visual SLAM. A sequence of images is used as the representation of places in our ROMS method, where each image in the sequence is encoded by multiple feature modalites so that different places can be recognized discriminatively. We formulate the robust place recognition problem as a convex optimization problem with structured sparsity regularization due to the fact that only a small set of template places can match the query place. In addition, we also develop a new algorithm to solve the formulated optimization problem efficiently, which guarantees to converge to the global optima theoretically. Our ROMS method is evaluated through extensive experiments on three large-scale benchmark datasets, which record scenes ranging from different times of the day, months, and seasons. Experimental results demonstrate that our ROMS method outperforms the existing loop closure detection methods in long-term SLAM, and achieves the state-of-the-art performance.
Links
- View publications from Fei Han
- View publications from Hua Wang
- View publications in the project, Mining Brain Imaging Genomics Data for Improved Cognitive Health
- View publications in the project, Mining Materials Genome Data for Prediction and Guidance of Nanoparticle Synthesis
- View publications researching Sparsity / Sparse Coding
- View publications applied to Computer Vision
Cite this paper
MLA
Han, Fei, et al. "Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM." Autonomous Robots 42.7 (2018): 1323-1335.
BibTeX
@article{han2018sequence,
title={Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM},
author={Han, Fei and Wang, Hua and Huang, Guoquan and Zhang, Hao},
journal={Autonomous Robots},
volume={42},
number={7},
pages={1323--1335},
year={2018},
publisher={Springer}
}