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Insect-inspired estimation of egomotion




Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and egomotion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates are of reasonable quality, albeit less reliable.

Author(s): Franz, MO. and Chahl, JS. and Krapp, HG.
Journal: Neural Computation
Volume: 16
Number (issue): 11
Pages: 2245-2260
Year: 2004
Month: November
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1162/0899766041941899
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Insect-inspired estimation of egomotion},
  author = {Franz, MO. and Chahl, JS. and Krapp, HG.},
  journal = {Neural Computation},
  volume = {16},
  number = {11},
  pages = {2245-2260},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2004},
  month_numeric = {11}