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Evaluating and Optimizing Feature Combinations for Visual Loop Closure Detection
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AI
DOI：10.1007/s10846-022-01575-7.png)
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En 中文
Loop closure detection (LCD) is a key step in visual simultaneous localization and mapping systems to correct the map and relocalize the vehicle. However, it may fail when illumination variations and shifting dynamics present in the scenes. One way to improve the precision is to effectively combine multiple image features that supply complementary information. In this paper, a general method to quantitatively measure the efficacy of individual image features as well as feature combinations for LCD is proposed by calculating the statistical distance considering the distributions of feature vectors. Based on different statistical distances including Kullback-Leibler divergence, Bhattacharyya divergence and Wasserstein metric, various numerical indices capable of evaluating feature combinations are obtained and compared. An unsupervised algorithm is further proposed to optimize feature combinations by maximizing any of the indices. Experiments show that the proposed indices can measure the efficacies of image features and the resulting feature combinations maximizing the indices can improve the precision of LCD.
Keyword:
Visual loop closure detection
Feature combination
Feature evaluation
Kullback-Leibler divergence
Bhattacharyya divergence
Wasserstein metric
期刊
J
IF:
2.8
论文数:
3.9K
被引数:
6.9K
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