Self feeding aerial robots, methods for detection and localization of standard wall outlets

Scott Morton; Ben Bosch; Nikolaos P Papanikolopoulos

doi:10.1109/MED.2016.7535864

Self feeding aerial robots, methods for detection and localization of standard wall outlets

Scott Morton, Ben Bosch, Nikolaos P Papanikolopoulos

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

This work describes computer vision methods developed for autonomous recharging of a quadrotor from a standard wall outlet. Specifically, this work encompasses two algorithms for detecting and tracking an outlet in a video stream acquired by a low cost quadrotor platform. Two different algorithms were developed for assumptions and requirements associated with different distances between the quadrotor and an outlet. The close-range algorithm achieves higher processing frequency and greater accuracy, but is effective within a limited distance to the outlet. The longrange detection algorithm requires greater processing time, but imposes fewer assumptions and can detect outlets at greater distances (lower resolution). The algorithms overlap in their effective ranges and therefore allow the system to continuously track from over 6 feet to within several inches of an outlet. The close-range algorithm has achieves a processing frequency greater than 15 Hz. The long-range detector accomplishes scale invariant hole detection with automatic outlet scale selection.

Original language	English (US)
Title of host publication	24th Mediterranean Conference on Control and Automation, MED 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	420-425
Number of pages	6
ISBN (Electronic)	9781467383455
DOIs	https://doi.org/10.1109/MED.2016.7535864
State	Published - Aug 5 2016
Event	24th Mediterranean Conference on Control and Automation, MED 2016 - Athens, Greece Duration: Jun 21 2016 → Jun 24 2016

Publication series

Name	24th Mediterranean Conference on Control and Automation, MED 2016

Other

Other	24th Mediterranean Conference on Control and Automation, MED 2016
Country/Territory	Greece
City	Athens
Period	6/21/16 → 6/24/16

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation through grants #IIP-0934327, #IIS-1017344, #IIP-1332133, #IIS-1427014, #IIP-1432957, #OISE-1551059, #CNS-1514626, #CNS-1531330, and #CNS-1544887.

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10.1109/MED.2016.7535864

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Morton, S., Bosch, B., & Papanikolopoulos, N. P. (2016). Self feeding aerial robots, methods for detection and localization of standard wall outlets. In 24th Mediterranean Conference on Control and Automation, MED 2016 (pp. 420-425). [7535864] (24th Mediterranean Conference on Control and Automation, MED 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MED.2016.7535864

Self feeding aerial robots, methods for detection and localization of standard wall outlets. / Morton, Scott; Bosch, Ben; Papanikolopoulos, Nikolaos P.
24th Mediterranean Conference on Control and Automation, MED 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 420-425 7535864 (24th Mediterranean Conference on Control and Automation, MED 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Morton, S, Bosch, B & Papanikolopoulos, NP 2016, Self feeding aerial robots, methods for detection and localization of standard wall outlets. in 24th Mediterranean Conference on Control and Automation, MED 2016., 7535864, 24th Mediterranean Conference on Control and Automation, MED 2016, Institute of Electrical and Electronics Engineers Inc., pp. 420-425, 24th Mediterranean Conference on Control and Automation, MED 2016, Athens, Greece, 6/21/16. https://doi.org/10.1109/MED.2016.7535864

Morton S, Bosch B, Papanikolopoulos NP. Self feeding aerial robots, methods for detection and localization of standard wall outlets. In 24th Mediterranean Conference on Control and Automation, MED 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 420-425. 7535864. (24th Mediterranean Conference on Control and Automation, MED 2016). doi: 10.1109/MED.2016.7535864

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abstract = "This work describes computer vision methods developed for autonomous recharging of a quadrotor from a standard wall outlet. Specifically, this work encompasses two algorithms for detecting and tracking an outlet in a video stream acquired by a low cost quadrotor platform. Two different algorithms were developed for assumptions and requirements associated with different distances between the quadrotor and an outlet. The close-range algorithm achieves higher processing frequency and greater accuracy, but is effective within a limited distance to the outlet. The longrange detection algorithm requires greater processing time, but imposes fewer assumptions and can detect outlets at greater distances (lower resolution). The algorithms overlap in their effective ranges and therefore allow the system to continuously track from over 6 feet to within several inches of an outlet. The close-range algorithm has achieves a processing frequency greater than 15 Hz. The long-range detector accomplishes scale invariant hole detection with automatic outlet scale selection.",

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note = "Funding Information: This material is based upon work supported by the National Science Foundation through grants #IIP-0934327, #IIS-1017344, #IIP-1332133, #IIS-1427014, #IIP-1432957, #OISE-1551059, #CNS-1514626, #CNS-1531330, and #CNS-1544887.; 24th Mediterranean Conference on Control and Automation, MED 2016 ; Conference date: 21-06-2016 Through 24-06-2016",

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AB - This work describes computer vision methods developed for autonomous recharging of a quadrotor from a standard wall outlet. Specifically, this work encompasses two algorithms for detecting and tracking an outlet in a video stream acquired by a low cost quadrotor platform. Two different algorithms were developed for assumptions and requirements associated with different distances between the quadrotor and an outlet. The close-range algorithm achieves higher processing frequency and greater accuracy, but is effective within a limited distance to the outlet. The longrange detection algorithm requires greater processing time, but imposes fewer assumptions and can detect outlets at greater distances (lower resolution). The algorithms overlap in their effective ranges and therefore allow the system to continuously track from over 6 feet to within several inches of an outlet. The close-range algorithm has achieves a processing frequency greater than 15 Hz. The long-range detector accomplishes scale invariant hole detection with automatic outlet scale selection.

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Self feeding aerial robots, methods for detection and localization of standard wall outlets

Abstract

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