Vision-based navigation system feature point selection method based on convex hull for non-cooperative target

Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods.

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Feature points selection with flocks of features constraint for visual simultaneous localization and mapping

International Journal of Advanced Robotic Systems ◽

10.1177/1729881416666784 ◽

2016 ◽

Vol 14 (1) ◽

pp. 172988141666678

Author(s):

Hong Liu ◽

Zhi Wang ◽

Pengjin Chen

Keyword(s):

Mobile Robots ◽

Simultaneous Localization And Mapping ◽

Selection Method ◽

Feature Point ◽

Feature Points ◽

Point Selection ◽

Localization Accuracy ◽

Surrounding Environment ◽

Localization And Mapping ◽

Crucial Problem

Simultaneous localization and mapping is a crucial problem for mobile robots, which estimates the surrounding environment (the map) and, at the same time, computes the robot location in it. Most researchers working on simultaneous localization and mapping focus on localization accuracy. In visual simultaneous localization and mapping , localization is to calculate the robot’s position relative to the landmarks, which corresponds to the feature points in images. Therefore, feature points are of importance to localization accuracy and should be selected carefully. This article proposes a feature point selection method to improve the localization accuracy. First, theoretical and numerical analyses are conducted to demonstrate the importance of distribution of feature points. Then, an algorithm using flocks of features is proposed to select feature points. Experimental results show that the proposed flocks of features selector implemented in visual simultaneous localization and mapping enhances the accuracy of both localization and mapping, verifying the necessity of feature point selection.

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Optimal base point selection method based on convex hull construction technology

E3S Web of Conferences ◽

10.1051/e3sconf/202124502034 ◽

2021 ◽

Vol 245 ◽

pp. 02034

Author(s):

Jian Dong ◽

Zhiqiang Zhang ◽

Lulu Tang ◽

Rencan Peng ◽

Hongchao Ji

Keyword(s):

Convex Hull ◽

Base Point ◽

Selection Method ◽

Optimal Selection ◽

Construction Technology ◽

Point Selection ◽

Base Points ◽

Selection Of ◽

The Ideal ◽

Hull Construction

The primary purpose of maritime delimitation is to ensure the maximum internal waters area obtained. In order to grantee the maximum internal waters area obtained with the selected base point, the idea and method of optimal selection of the territorial sea base points with the convex hull (minimum convex hull) construction technology is proposed. The ideal base points are selected by constructing convex hull for all alternative base points, which makes it possible to realize the automatic selection of base points under the principle of the maximum internal waters area.

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