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ICRA
2006
IEEE

Integration of Visual and Inertial Information for Egomotion: a Stochastic Approach

13 years 9 months ago
Integration of Visual and Inertial Information for Egomotion: a Stochastic Approach
— We present a probabilistic framework for visual correspondence, inertial measurements and Egomotion. First, we describe a simple method based on Gabor filters to produce correspondence probability distributions. Next, we generate a noise model for inertial measurements. Probability distributions over the motions are then computed directly from the correspondence distributions and the inertial measurements. We investigate combining the inertial and visual information for a single distribution over the motions. We find that with smaller amounts of correspondence information, fusion of the visual data with the inertial sensor results in much better Egomotion estimation. This is essentially because inertial measurements decrease the ”translation-rotation” ambiguity. However, when more correspondence information is used, this ambiguity is reduced to such a degree that the inertial measurements provide negligible improvement in accuracy. This suggests that inertial and visual infor...
Justin Domke, Yiannis Aloimonos
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICRA
Authors Justin Domke, Yiannis Aloimonos
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