AI/ML

Learning to navigate in cities without a map

How did you learn to navigate the neighborhood of your childhood, to go to a friends house, to your school or to the grocery store? Probably without a map and simply by remembering the visual appearance of streets and turns along the way. As you gradually explored your neighborhood, you grew more confident, mastered your whereabouts and learned new and increasingly complex paths. You may have gotten briefly lost, but found your way again thanks to landmarks, or perhaps even by looking to the sun for an impromptu compass.Navigation is an important cognitive task that enables humans and animals to traverse, without maps, over long distances in a complex world. Such long-range navigation can simultaneously support self-localisation (I am here) and a representation of the goal (I am going there).In Learning to Navigate in Cities Without a Map,we present an interactive navigation environment that uses first-person perspective photographs from Google Street View,approved for use by the StreetLearn project and academic research, and gamify that environment to train an AI. As standard with Street View images, faces and license plates have been blurred and are unrecognisable. We build a neural network-based artificial agent that learns to navigate multiple cities using visual information (pixels from a Street View image).

Source link

Related posts

Mixed-type data analysis II: Pairwise models

Newsemia

CoParenter helps divorced parents settle disputes using AI and human mediation

Newsemia

Chapter 7: The Human Story [Are we a part of the world or apart from the world?]

Newsemia

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy

COVID-19

COVID-19 (Coronavirus) is a new illness that is having a major effect on all businesses globally LIVE COVID-19 STATISTICS FOR World