Journal
APPLIED SCIENCES-BASEL
Volume 9, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/app9061120
Keywords
loop closure detection; convolutional neural network; spatial pyramid pooling
Categories
Funding
- National Natural Science Foundation of China [61403423]
- Natural Science Foundation of Hunan Province of China [2018JJ3689]
- National Key Research and Development Plan [2018YFB1201602]
- Science and Technology Major Project of Hunan Province of China [2017GK1010]
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Loop closure detection plays a very important role in the mobile robot navigation field. It is useful in achieving accurate navigation in complex environments and reducing the cumulative error of the robot's pose estimation. The current mainstream methods are based on the visual bag of word model, but traditional image features are sensitive to illumination changes. This paper proposes a loop closure detection algorithm based on multi-scale deep feature fusion, which uses a Convolutional Neural Network (CNN) to extract more advanced and more abstract features. In order to deal with the different sizes of input images and enrich receptive fields of the feature extractor, this paper uses the spatial pyramid pooling (SPP) of multi-scale to fuse the features. In addition, considering the different contributions of each feature to loop closure detection, the paper defines the distinguishability weight of features and uses it in similarity measurement. It reduces the probability of false positives in loop closure detection. The experimental results show that the loop closure detection algorithm based on multi-scale deep feature fusion has higher precision and recall rates and is more robust to illumination changes than the mainstream methods.
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