Fusion of semantic and appearance features for loop‐closure detection in a dynamic environment
Abstract Loop‐closure detection is important in large‐scale localisation system. However, it is still difficult in a dynamic environment. An online fast loop‐closure detection algorithm based on Deeplabv3 with MobileNetV2 (DpMn2) as network backbone and local difference binary descriptor is proposed...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/91e8951d056d47b99ba61deb0c0e8171 |
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| Sumario: | Abstract Loop‐closure detection is important in large‐scale localisation system. However, it is still difficult in a dynamic environment. An online fast loop‐closure detection algorithm based on Deeplabv3 with MobileNetV2 (DpMn2) as network backbone and local difference binary descriptor is proposed, and the algorithm is named as DpMn2‐LDB. DpMn2 splits out common dynamic objects of images, and then uses visual geometry group network (VGG16) that is trained on place‐centric data to extract global features for nearest neighbour image retrieval. The loop‐closure matches are verified based on LDB descriptors and random sample consensus (RANSAC). Experimental results show that the proposed method can obtain a higher recall rate under 100% precision with less execution time per frame on several public datasets compared with other typical or state‐of‐the‐art algorithms. |
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