Learning Integrated Holism-Landmark Representations for Long-Term Loop Closure Detection
Fei Han, Hua Wang, Hao Zhang
AAAI - 2018
Loop closure detection is a critical component of large-scale simultaneous localization and mapping (SLAM) in loopy environments. This capability is challenging to achieve in long-term SLAM, when the environment appearance exhibits significant long-term variations across various time of the day, months, and even seasons. In this paper, we introduce a novel formulation to learn an integrated long-term representation based upon both holistic and landmark information, which integrates two previous insights under a unified framework: (1) holistic representations outperform keypoint-based representations, and (2) landmarks as an intermediate representation provide informative cues to detect challenging locations. Our new approach learns the representation by projecting input visual data into a low-dimensional space, which preserves both the global consistency (to minimize representation error) and the local consistency (to preserve landmarks’ pairwise relationship) of the input data. To solve the formulated optimization problem, a new algorithm is developed with theoretically guaranteed convergence. Extensive experiments have been conducted using two large-scale public benchmark data sets, in which the promising performances have demonstrated the effectiveness of the proposed approach.
Links
- View publications from Fei Han
- View publications from Hua Wang
- View publications presented in AAAI
- 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 Embeddings
- View publications researching Robust Learning Models
- View publications applied to Computer Vision
Cite this paper
MLA
Han, Fei, Hua Wang, and Hao Zhang. "Learning Integrated Holism-Landmark Representations for Long-Term Loop Closure Detection." Thirty-Second AAAI Conference on Artificial Intelligence. 2018.
BibTeX
@inproceedings{han2018learning,
title={Learning Integrated Holism-Landmark Representations for Long-Term Loop Closure Detection},
author={Han, Fei and Wang, Hua and Zhang, Hao},
booktitle={Thirty-Second AAAI Conference on Artificial Intelligence},
year={2018}
}