[1]ZENG Yujing,JIANG Yong.Feature selection simultaneous localization and mapping algorithm incorporating attention and anticipation[J].CAAI Transactions on Intelligent Systems,2021,16(6):1039-1044.[doi:10.11992/tis.202010036]
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Feature selection simultaneous localization and mapping algorithm incorporating attention and anticipation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 6 Page number: 1039-1044 Column: 学术论文—机器感知与模式识别 Public date: 2021-11-05
Title:
Feature selection simultaneous localization and mapping algorithm incorporating attention and anticipation
Author(s):
ZENG Yujing1; 2; 3; 4;  JIANG Yong2; 3; 4
1. School of Information Science and Engineering, Northeastern University, Shenyang 110006, China;
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
3. Key Laboratory of Networked Control Systems, Chinese Acad
Keywords:
SLAM; vision; attention; anticipation; feature selection; logdet metric; lazy evaluation; greedy algorithm; information matrix
CLC:
TP391
DOI:
10.11992/tis.202010036
Abstract:
A simultaneous localization and mapping (SLAM) algorithm incorporating attention and anticipation is proposed to solve the localization failure problem of SLAM in the scene of sharp turning and fast movement. The algorithm can select feature points that are more likely to remain in the field of view as the camera moves and discard features that are about to disappear from the field of view. The logdet metric is used to measure the feasibility of quantifying the feature selection first. The information matrix of the feature points is then calculated. From the detected features, a greedy algorithm is used to select k features (approximately) to maximize the logdet metric. The actual test combined with ORB-SLAM2 shows that the algorithm can ensure positioning accuracy in complex scenarios, such as in the scene of sharp turning and fast movement.
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