Researchers have developed new
methods for simultaneous localization and mapping (SLAM) that can be used to
construct or update maps of a given environment in real time, while
simultaneously tracking an artificial agent or robot's location within these
maps. Most existing SLAM approaches rely heavily on the use of range-based or
vision-based sensors, both to sense the environment and a robot's movements.
These sensors can be very expensive and typically require significant
computational power to operate properly. Researchers at the Singapore
University of Technology and Design, Southwest University of Science and
Technology, the University of Moratuwa and Nanyang Technological University
have recently developed a new technique for collaborative SLAM that does rely
on range-based or vision-based sensors which could enable more effective robot
navigation within unknown indoor environments at a lower cost than that of most
previously proposed methods. Researchers developed an approach for
collaborative simultaneous localization and radio fingerprint mapping called
C-SLAM-RF. Their technique works by crowdsensing Wi-Fi measurements in large
indoor environments and then using these measurements to generate maps or
locate artificial agents.
The system developed by
researchers receives information about the strength of the signal coming from
pre-existing Wi-Fi access points spread around a given environment, as well as
from pedestrian dead reckoning (PDR) processes (i.e., calculations of someone's
current position) derived from a smart phone. It then uses these signals to
build a map of the environment without requiring prior knowledge of the
environment or the distribution of the access points within it. The C-SLAM-RF
tool devised by the researchers can also determine whether the robot has
returned to a previously visited location, known as ‘loop closure’, by
assessing the similarity between different signals' radio fingerprints.
Researchers tested their technique in an indoor environment with an area of 130
meters x 70 meters. Their results were highly promising, as their system's
performance exceeded that of several other existing techniques for SLAM, often
by a considerable margin. In the future, the approach for collaborative SLAM
devised by this team of researchers could help to enhance robot navigation in
unknown environments. In addition, the fact that it does not require the use of
expensive sensors and relies on existing Wi-Fi hotspots makes it a more
feasible solution for large-scale implementations.
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