1,316 research outputs found
Vision-Based Localization Algorithm Based on Landmark Matching, Triangulation, Reconstruction, and Comparison
Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRC) global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data
Self-localization based on Image Features of Omni-directional Image
Omni-vision system using an omni-mirror is popular
to acquire environment information around an autonomous
mobile robot. In RoboCup soccer middle size robot league in particular,
self-localization methods based on white line extraction
on the soccer field are popular. We have studied a self-localization
method based on image features, for example, SIFT and SURF,
so far. Comparative studies with a conventional self-localization
method based on white line extraction are conducted. Compared
to the self-localization method based on white line extraction,
the method based on image feature can be applied to a general
environment with a compact database
Design and implementation of a multi-octave-band audio camera for realtime diagnosis
Noise pollution investigation takes advantage of two common methods of
diagnosis: measurement using a Sound Level Meter and acoustical imaging. The
former enables a detailed analysis of the surrounding noise spectrum whereas
the latter is rather used for source localization. Both approaches complete
each other, and merging them into a unique system, working in realtime, would
offer new possibilities of dynamic diagnosis. This paper describes the design
of a complete system for this purpose: imaging in realtime the acoustic field
at different octave bands, with a convenient device. The acoustic field is
sampled in time and space using an array of MEMS microphones. This recent
technology enables a compact and fully digital design of the system. However,
performing realtime imaging with resource-intensive algorithm on a large amount
of measured data confronts with a technical challenge. This is overcome by
executing the whole process on a Graphic Processing Unit, which has recently
become an attractive device for parallel computing
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