22,406 research outputs found
Learning to Fly by Crashing
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: https://youtu.be/u151hJaGKU
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Grid maps are widely used in robotics to represent obstacles in the
environment and differentiating dynamic objects from static infrastructure is
essential for many practical applications. In this work, we present a methods
that uses a deep convolutional neural network (CNN) to infer whether grid cells
are covering a moving object or not. Compared to tracking approaches, that use
e.g. a particle filter to estimate grid cell velocities and then make a
decision for individual grid cells based on this estimate, our approach uses
the entire grid map as input image for a CNN that inspects a larger area around
each cell and thus takes the structural appearance in the grid map into account
to make a decision. Compared to our reference method, our concept yields a
performance increase from 83.9% to 97.2%. A runtime optimized version of our
approach yields similar improvements with an execution time of just 10
milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and
it was accapted at IV 201
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