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Feature optimization for long-range visual homing in changing environments.

Zhu Q, Liu X, Cai C - Sensors (Basel) (2014)

Bottom Line: In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments.To verify the feasibility of the proposal, several comprehensive evaluations are conducted.The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

View Article: PubMed Central - PubMed

Affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China. zhuqidan@hrbeu.edu.cn.

ABSTRACT
This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

No MeSH data available.


The homing trajectories in randomly changing environments. (a–d) show the results obtained in phase 1, 2, 3 and 4, respectively. Red lines correspond to the proposal. Violet lines correspond to SURF. Blue lines correspond to the proposal without selection and updating. Green lines correspond to the proposal without updating. In each figure, the robot moves from left to right, the black disks with the same size are intermediate nodes, and the bigger one is starting node or home node.
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f12-sensors-14-03342: The homing trajectories in randomly changing environments. (a–d) show the results obtained in phase 1, 2, 3 and 4, respectively. Red lines correspond to the proposal. Violet lines correspond to SURF. Blue lines correspond to the proposal without selection and updating. Green lines correspond to the proposal without updating. In each figure, the robot moves from left to right, the black disks with the same size are intermediate nodes, and the bigger one is starting node or home node.

Mentions: In order to demonstrate the effectiveness of each part of the feature optimization procedure, our approach was compared with three other approaches. The experimental results are presented in Figures 8,9, 10,11–12. The results of the proposed approach and the standard SURF are shown by red and violet, respectively. The blue indicates the results obtained by our approach without the procedure of feature selection and updating. And the results shown by green are obtained by our approach without the procedure of feature updating. The decrease of curvature and the improvement on smoothness can be chosen as criteria to the effectiveness of the proposed approach on homing accuracy.


Feature optimization for long-range visual homing in changing environments.

Zhu Q, Liu X, Cai C - Sensors (Basel) (2014)

The homing trajectories in randomly changing environments. (a–d) show the results obtained in phase 1, 2, 3 and 4, respectively. Red lines correspond to the proposal. Violet lines correspond to SURF. Blue lines correspond to the proposal without selection and updating. Green lines correspond to the proposal without updating. In each figure, the robot moves from left to right, the black disks with the same size are intermediate nodes, and the bigger one is starting node or home node.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958223&req=5

f12-sensors-14-03342: The homing trajectories in randomly changing environments. (a–d) show the results obtained in phase 1, 2, 3 and 4, respectively. Red lines correspond to the proposal. Violet lines correspond to SURF. Blue lines correspond to the proposal without selection and updating. Green lines correspond to the proposal without updating. In each figure, the robot moves from left to right, the black disks with the same size are intermediate nodes, and the bigger one is starting node or home node.
Mentions: In order to demonstrate the effectiveness of each part of the feature optimization procedure, our approach was compared with three other approaches. The experimental results are presented in Figures 8,9, 10,11–12. The results of the proposed approach and the standard SURF are shown by red and violet, respectively. The blue indicates the results obtained by our approach without the procedure of feature selection and updating. And the results shown by green are obtained by our approach without the procedure of feature updating. The decrease of curvature and the improvement on smoothness can be chosen as criteria to the effectiveness of the proposed approach on homing accuracy.

Bottom Line: In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments.To verify the feasibility of the proposal, several comprehensive evaluations are conducted.The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

View Article: PubMed Central - PubMed

Affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China. zhuqidan@hrbeu.edu.cn.

ABSTRACT
This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

No MeSH data available.