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.


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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.
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  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},