Discriminative and Semantic Feature Selection for Place Recognition towards Dynamic Environments
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Abstract (arXiv)
Features play an important role in various visual tasks, especially in visual place recognition applied in perceptual changing environments. In this paper, we address the challenges of place recognition due to dynamics and confusable patterns by proposing a discriminative and semantic feature selection network, dubbed as DSFeat. Supervised by both semantic information and attention mechanism, we can estimate pixel-wise stability of features, indicating the probability of a static and stable region from which features are extracted, and then select features that are insensitive to dynamic interference and distinguishable to be correctly matched. The designed feature selection model is evaluated in place recognition and SLAM system in several public datasets with varying appearances and viewpoints. Experimental results conclude that the effectiveness of the proposed method. It should be noticed that our proposal can be readily pluggable into any feature-based SLAM system.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 18 Mar 2021.
Builds on
23 of 50 references.
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11 more
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