Appearance-based loop closure detection combining lines and learned points for low-textured environments
نویسندگان
چکیده
Abstract Hand-crafted point descriptors have been traditionally used for visual loop closure detection. However, in low-textured environments, it is usually difficult to find enough features and, hence, the performance of such algorithms degrade. Under this context, paper proposes a detection method that combines lines and learned points work, particularly, scenarios where hand-crafted fail. To index previous images, we adopt separate incremental binary Bag-of-Words (BoW) schemes lines. Moreover, binarization procedure features’ benefit from advantages into BoW model. Furthermore, image candidates each instance are merged using novel query-adaptive late fusion approach. Finally, spatial verification stage, which integrates appearance geometry perspectives, allows us enhance global method. Our approach validated several public datasets, outperforming other state-of-the-art solutions most cases, especially scenarios.
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ژورنال
عنوان ژورنال: Autonomous Robots
سال: 2022
ISSN: ['0929-5593', '1573-7527']
DOI: https://doi.org/10.1007/s10514-021-10032-7