28 January 2020

Wi-Fi Collaborative SLAM

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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