NDLI: Ceiling vision based localizer for mobile robot

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Ceiling vision based localizer for mobile robot

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Ceiling vision based localizer for mobile robot

Content Provider	ACM Digital Library
Author	Jun, Sewoong Park, Changwoo Kim, Seung-Hun
Abstract	When mobile robots perform their missions, the self-localization is needed basically. Several past researches established how to obtain their location information from the environment by using a distance sensor or a camera. However, these methods have map-making problem when the environment changes and localization problem while the robot moves from sensing features has typical affine and occlusion characteristics. This paper presents a localizer for mobile robot that travels around indoor environments. Our module uses the only one sensor, a single camera looking up the ceiling. There is no efficient enough SLAM* (Simultaneous Localization And Mapping) algorithm working on embedded system. The initial difficulty of vision based SLAM is computational complexity to acquire reliable feature on their algorithm. To reduce the computational complexity, we use the ceiling segmentation to extract line features of ceiling area. Line features are extracted from the boundaries between the ceiling and walls. The line features have advantages over point features for its robustness to environmental variation and structural information helpful to data association. Extended Kalman Filter is used to estimate the pose of a robot and build the ceiling map with line features. The experiment is practiced in our indoor test bed and the proposed algorithm is proved by the experimental results. *SLAM: Simultaneous localization and mapping is a technique used by robots and autonomous vehicles to build up a map within an unknown environment or to update a map within a known environment while at the same time keeping track of their current location.
Starting Page	1
Ending Page	1
Page Count	1
File Format	PDF
ISBN	9781450306812
DOI	10.1145/1999320.1999374
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-23
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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