Robust multimodal sequence-based loop closure detection via structured sparsity

Fei Han, Hao Zhang, Hua Wang

RSS - 2016

Loop closure detection is an essential component for simultaneously localization and mapping in a variety of robotics applications. One of the most challenging problems is to perform long-term place recognition with strong perceptual aliasing and appearance variations due to changes of illumination, vegetation, weather, etc. To address this challenge, we propose a novel Robust Multimodal Sequence-based (ROMS) method for long-term loop closure detection, by formulating image sequence matching as an optimization problem regularized by structured sparsity-inducing norms. Our method is able to model the sparsity nature of place recognition, i.e., the current location should match only a small subset of previously visited places, as well as to model underlying structures of image sequences and incorporate multiple feature modalities to construct a discriminative scene representation. In addition, a new optimization algorithm is developed to efficiently solve the formulated problem, which has a theoretical guarantee to converge to the global optimal solution. To evaluate the ROMS algorithm, extensive experiments are performed using large-scale benchmark datasets, including St Lucia, CMU-VL, and Nordland datasets. Experimental results have validated that our algorithm outperforms previous loop closure detection methods, and obtains the state-of-the-art performance on long-term place recognition.


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Zhang, Hao, Fei Han, and Hua Wang. "Robust Multimodal Sequence-Based Loop Closure Detection via Structured Sparsity." Robotics: Science and systems. 2016.
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  title={Robust Multimodal Sequence-Based Loop Closure Detection via Structured Sparsity.},
  author={Zhang, Hao and Han, Fei and Wang, Hua},
  booktitle={Robotics: Science and systems},