Estimation With Fast Feature Selection in Robot Visual Navigation

We consider the robot localization problem with sparse visual feature selection. The underlying key property is that contributions of trackable features (landmarks) appear linearly in the information matrix of the corresponding estimation problem. We utilize standard models for motion and vision sys...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 5; no. 2; pp. 3572 - 3579
Main Authors: Mousavi, Hossein K., Motee, Nader
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Autonomous agents
Computer simulation
Covariance matrices
Feature extraction
localization
Mathematical models
Navigation
Robot localization
Robots
Vision systems
visual-based navigation
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:We consider the robot localization problem with sparse visual feature selection. The underlying key property is that contributions of trackable features (landmarks) appear linearly in the information matrix of the corresponding estimation problem. We utilize standard models for motion and vision system using a camera to formulate the feature selection problem over moving finite-time horizons. We propose a scalable randomized sampling algorithm to select more informative features to obtain a certain estimation quality. We provide probabilistic performance guarantees for our method. The time-complexity of our feature selection algorithm is linear in the number of candidate features, which is practically plausible and outperforms existing greedy methods that scale quadratically with the number of candidate features. Our numerical simulations confirm that not only the execution time of our proposed method is comparably less than that of the greedy method, but also the resulting estimation quality is very close to the greedy method.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.2974654