Deep Neural Network for Real-Time Autonomous Indoor Navigation
release_4pxfrsk5ybgn7msddlfmutcwdy
by
Dong Ki Kim, Tsuhan Chen
2015
Abstract
Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many
challenges. One main reason is that GPS has limited precision in indoor
environments. The additional fact that MAVs are not able to carry heavy weight
or power consuming sensors, such as range finders, makes indoor autonomous
navigation a challenging task. In this paper, we propose a practical system in
which a quadcopter autonomously navigates indoors and finds a specific target,
i.e., a book bag, by using a single camera. A deep learning model,
Convolutional Neural Network (ConvNet), is used to learn a controller strategy
that mimics an expert pilot's choice of action. We show our system's
performance through real-time experiments in diverse indoor locations. To
understand more about our trained network, we use several visualization
techniques.
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