Texas Instruments The Future of Robotics | 17
A person walking in front of an mmWave sensor generates
multiple reflection points. Each of these detected points can
be mapped in a 3D field relative to the sensor (as shown
in Figure 8) within the popular robot operating system
visualization (RVIZ) visualization tool. This mapping collects
all points over a quarter-of-a-second time period. The density
of the point information collected provides a good amount of
fidelity with leg and arm movement visible, enabling object
classification as a moving person. The clarity of the open
spaces in the 3D field is also very important data for mobile
robots so that they can operate autonomously.
Mapping and navigation using mmWave sensors
Using the point information for objects detected by the
IWR1443BOOST EVM, it is then possible to demonstrate the
use on mmWave radar as the only sensor to accurately map
obstacles in a room, and to use the free space identified for
autonomous operation. There are several robotic open-source
Figure 8: Point cloud of a person shown in RVIZ, captured with the IWR1443BOOST EVM.
communities, including Robot OS (ROS) and Arduino. To quickly
demonstrate the use of a single mmWave radar in mapping
and navigation applications, we chose Robot OS and mounted
the IWR1443BOOST EVM on the ROS community's Turtlebot 2
development platform, as shown in Figure 9.
Figure 9: IWR1443BOOST EVM mounted on a Turtlebot 2.
Accurate odometry
information is essential for
the autonomous movement
of a robot platform.
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