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Information-Driven Active Audio-Visual Source Localization.

Schult N, Reineking T, Kluss T, Zetzsche C - PLoS ONE (2015)

Bottom Line: These actions by the robot successively reduce uncertainty about the source's position.Because of the robot's mobility, this approach is suitable for use in complex and cluttered environments.We present qualitative and quantitative results of the system's performance and discuss possible areas of application.

View Article: PubMed Central - PubMed

Affiliation: Cognitive Neuroinformatics, Bremen University, Bremen, Germany.

ABSTRACT
We present a system for sensorimotor audio-visual source localization on a mobile robot. We utilize a particle filter for the combination of audio-visual information and for the temporal integration of consecutive measurements. Although the system only measures the current direction of the source, the position of the source can be estimated because the robot is able to move and can therefore obtain measurements from different directions. These actions by the robot successively reduce uncertainty about the source's position. An information gain mechanism is used for selecting the most informative actions in order to minimize the number of actions required to achieve accurate and precise position estimates in azimuth and distance. We show that this mechanism is an efficient solution to the action selection problem for source localization, and that it is able to produce precise position estimates despite simplified unisensory preprocessing. Because of the robot's mobility, this approach is suitable for use in complex and cluttered environments. We present qualitative and quantitative results of the system's performance and discuss possible areas of application.

No MeSH data available.


Localization performance.The result show a significantly lower number of actions required to reach a criterion RMS error < 30cm in both A) simulation and B) robot experiments. Errors bars show confidence intervals (.95).
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pone.0137057.g009: Localization performance.The result show a significantly lower number of actions required to reach a criterion RMS error < 30cm in both A) simulation and B) robot experiments. Errors bars show confidence intervals (.95).

Mentions: Moreover, the number of actions required to reach a criterion RMS error of less than 30cm serves as an indicator of localization performance. As shown in Fig 9A, the IG procedure requires fewer actions to reach the criterion in comparison to randomly selected movements. This impression from descriptive statistics is confirmed by a Mann-Whitney test (α = .5) which indicates a significantly lower number of actions using the IG procedure (Mdn = 2) compared to random selection of movements (Mdn = 5), U = −8.67, p <.001.


Information-Driven Active Audio-Visual Source Localization.

Schult N, Reineking T, Kluss T, Zetzsche C - PLoS ONE (2015)

Localization performance.The result show a significantly lower number of actions required to reach a criterion RMS error < 30cm in both A) simulation and B) robot experiments. Errors bars show confidence intervals (.95).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556528&req=5

pone.0137057.g009: Localization performance.The result show a significantly lower number of actions required to reach a criterion RMS error < 30cm in both A) simulation and B) robot experiments. Errors bars show confidence intervals (.95).
Mentions: Moreover, the number of actions required to reach a criterion RMS error of less than 30cm serves as an indicator of localization performance. As shown in Fig 9A, the IG procedure requires fewer actions to reach the criterion in comparison to randomly selected movements. This impression from descriptive statistics is confirmed by a Mann-Whitney test (α = .5) which indicates a significantly lower number of actions using the IG procedure (Mdn = 2) compared to random selection of movements (Mdn = 5), U = −8.67, p <.001.

Bottom Line: These actions by the robot successively reduce uncertainty about the source's position.Because of the robot's mobility, this approach is suitable for use in complex and cluttered environments.We present qualitative and quantitative results of the system's performance and discuss possible areas of application.

View Article: PubMed Central - PubMed

Affiliation: Cognitive Neuroinformatics, Bremen University, Bremen, Germany.

ABSTRACT
We present a system for sensorimotor audio-visual source localization on a mobile robot. We utilize a particle filter for the combination of audio-visual information and for the temporal integration of consecutive measurements. Although the system only measures the current direction of the source, the position of the source can be estimated because the robot is able to move and can therefore obtain measurements from different directions. These actions by the robot successively reduce uncertainty about the source's position. An information gain mechanism is used for selecting the most informative actions in order to minimize the number of actions required to achieve accurate and precise position estimates in azimuth and distance. We show that this mechanism is an efficient solution to the action selection problem for source localization, and that it is able to produce precise position estimates despite simplified unisensory preprocessing. Because of the robot's mobility, this approach is suitable for use in complex and cluttered environments. We present qualitative and quantitative results of the system's performance and discuss possible areas of application.

No MeSH data available.