Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - A Real-World Test of a Neural Model
Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to esti- mate self-motion from the optic flow. We present a theory for the con- struction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distri- bution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirec- tional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas transla- tion estimates turn out to be less reliable.
Cite
Text
Franz and Chahl. "Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - A Real-World Test of a Neural Model." Neural Information Processing Systems, 2002.Markdown
[Franz and Chahl. "Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - A Real-World Test of a Neural Model." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/franz2002neurips-linear/)BibTeX
@inproceedings{franz2002neurips-linear,
title = {{Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - A Real-World Test of a Neural Model}},
author = {Franz, Matthias O. and Chahl, Javaan S.},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {1343-1350},
url = {https://mlanthology.org/neurips/2002/franz2002neurips-linear/}
}