Abstract:
Deep learning has made exciting advances is multiple domains. One major
reason of success is the availability of big data. However, gathering lot of
data with human e ort introduces lot of biases in the dataset. The recogni-
tion community has started investigating these biases. This has helped them
to mitigate the a ect of such biases on the recognition system. In this work
we plan to investigate such biases in visual navigation datasets.
Visual navigation datasets involve specialized sensors (sensors, lasers,
stereo cameras). It is di cult to crowd source data for these sensors as com-
pared to simple monocular camera. Therefore, datasets are gathered under
the supervision of only few individuals. This lack of diversity in the collection
stage might result is stronger biases in the visual navigation datasets. We
plan to investigate these biases.
Furthermore, realistic simulators are gaining traction recently. We plan to
investigate the recent realistic simulators and determine their simulation to
reality gap. This investigation is required to build visual navigation models
that will assist in developing predictable navigation methods for autonomous
agents beyond lab settings.
