IEEE Robotics & Automation Magazine - June 2015 - 31

x Position (m)
y Position (m)
z Position (m)

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0
-1
-2

Desired
Tango
Fused

0

1

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Time (s)
(a)

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(b)

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(c)

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Vy (m/s)

Vx (m/s)

Figure 11. The Cartesian positions of the vehicle with the desired
trajectory (blue), Tango estimates (red), and UKF estimates (green).
(a) x position Cartesian component, (b) y position Cartesian
component, and (c) z position Cartesian component.

Vz (m/s)

Figure 13. A representative map created by two vehicles using our
RGB-D framework.

Fused
Desired

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(a)

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(c)

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0.1
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-0.1

Figure 12. The Cartesian velocities of the vehicle with the desired
trajectory (blue) and UKF estimates (green). (a) x velocity Cartesian
component, (b) y velocity Cartesian component, and (c) z velocity
Cartesian component.

the multimedia content available at https://www.youtube.
com/watch?v=WuPZeD2J3pw, the extensive experiments
are presented, with different trajectory profiles and maximum velocities reaching values up to 1.5 m/s.

Figure 14. A representative dense map created using our RGB-D
framework suggests that this vision is likely to become a reality.

Conclusions
In this article, we demonstrate the hardware and software architecture with the underlying algorithms to enable the plug-andplay functionality with a smartphone and quadrotor. In addition,
we demonstrated the autonomous navigation of a quadrotor
platform. With data from an external motion-capture system to
measure ground truth and desired trajectory values, we show
that the robot can navigate in 3-D at speeds of 1-1.5 m/s with an
average error of 4 cm in position. Furthermore, we have shown
that the system is robust and can recover from external disturbances of 0.7 m. Both these facts show that the system is suitable
for reliable indoor navigation. A simple user interface, possibly
from another handheld platform, can be used to guide the robot
to designated points in a map or to generate a 3-D map. This
opens up the vision of a flying robot for every human user (the
number of cell phones in the world is already comparable with
the world population), and the potential of using aerial robots to
generate 3-D maps of buildings, localize sensors in smart buildings, take pictures from different viewpoints, and assist humans
in search and rescue applications. Thus, we strongly believe that
smartphones will have a strong impact in real life, in terms of advances in the automation of processes such as construction,
package delivery, and interaction between humans. The interest
of companies such as Google, Apple, and Qualcomm in smartphones equipped with such sensors as 3-D cameras and IMUs
suggests that this vision is likely to become a reality.
While the prototype described in this article requires the use of
an additional processor to facilitate integration, there is no reason
why we should not be able to use the onboard processor on the
smartphone. In the near future, this would allow any user the
June 2015

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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31
https://www.youtube
Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - June 2015
