IEEE Robotics & Automation Magazine - June 2015 - 26

Figure 3. The Google Project Tango device.

[Google (http://www.google.com) 2014; see Figure 3]. The
smartphone incorporates an enhanced RGB-D camera with a
fisheye lens that has a field of view of 170c and a depth sensor
able to capture a dense set of point clouds. It also incorporates
customized hardware and software designed to track full 3-D
motion while concurrently creating a map of the environment
using visual odometry and structure from motion algorithms.
These sensors allow the phone to make more than a quarter of a
million 3-D measurements every second, updating its position
and orientation in real time and combining the data into a single
3-D model of the surrounding space. In comparison, previous
works on implementing vision-based algorithms on camera
phones are based on marker tracking [21] and localization algorithms [22]. These algorithms are designed for augmented-reality applications; they employ cameras with a limited frame rate
and with a small field of view. For these reasons, they are not
suitable to deal with the long-term operations and large navigation coverage areas needed in robotic tasks.
In this article, a complete architecture representing a first step
toward autonomous aerial robot flight with a camera phone is

presented. It represents the first "plug-and-play" integration of a
consumer product with an off-the-shelf aerial robot to enable
autonomy with possible onboard localization, mapping, and
control. Thus, it is representative a new class of affordable smart
devices that can potentially lower the barrier to automation in
homes by providing services for localization, state estimation,
control, and mapping. In the future, end users may be able to
utilize their smartphone device to autonomously control an aerial platform and to add new functionalities. The first contribution of this work is the development of a quadrotor platform
equipped with the Google Project Tango [20] smartphone sensor and a small processor unit. The second contribution is the
vehicle's control based on smartphone localization estimation. A
nonlinear controller guarantees the exponential stability of the
platform, which is able to follow trajectories in 3-D space.
Finally, the fusion of the phone's pose with inertial sensor measurements allows for an increased rate of state estimation, and,
thus, it enables fast motions.
System Architecture
Our platform of choice was a quadrotor due to its mechanical
simplicity [5] and ease of control. Moreover, its ability to operate in confined spaces, hover at any given point in space, and
perch or land on a flat surface makes it a very attractive aerial
platform with tremendous potential. A description of the proposed hardware and software architecture is presented here. A
schema of the proposed approach is shown in Figure 4.

Hardware Architecture
The experimental platform shown in Figure 5 is made from
COTS components and is equipped with an AutoPilot board consisting of an IMU and a user-programmable ARM7 microcontroller. The main computation unit on board is an ODROID-XU
(http://www.hardkernel.com) with a 1.7-GHz Samsung Exynos 5
Octa processor with 2 GB of random access memory (RAM), a
16-GB embedded multimedia controller (eMMC) module, and a
802.11n Wi-Fi transceiver. The only other addition to this setup is
a forward-pointing Project Tango [20] smartphone from Google
and a USB 3.0 cable for communication between the ODROIDXU and the Tango phone. The total
mass of the platform is 900 g. The
software framework is presented in
Base Station
Robot
the "Software Architecture" section.
Visualization
Rc, Xc
Position
Trajectory xd, vd
It should be noted that our exSO (3)
and
Controller
Planning
Controller
perimental setup is independent of
Navigation Settings
the specifics of the employed emSoftware Architecture
bedded board. The processor usage
is estimated to be 35% of the total
Mi, x
available central processing unit
UKF
Tango
Plant
ODROID-XU
Estimation
(CPU), which suggests that a
Phone
smaller and less powerful embedHardware Architecture
ded processor will suffice.
However, to guarantee a reliable
x, v, R, X
setup and to reuse the same configuration for other robotic tasks, we
Figure 4. The system architecture for specification, planning, control, and estimation. UKF:
choose to use the ODROID-XU
unscented Kalman filter.
26

IEEE ROBOTICS & AUTOMATION MAGAZINE

June 2015

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Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - June 2015