Abstract:
Simultaneous Localization and Mapping (SLAM) is the process of learning about both the environment and about a robot's location with respect to the environment and is ess...Show MoreMetadata
Abstract:
Simultaneous Localization and Mapping (SLAM) is the process of learning about both the environment and about a robot's location with respect to the environment and is essential for robots to autonomously navigate. A variety of algorithms using many different sensors such as RGB-D cameras, laser range finders, ultrasonic sensors and others have been proposed to perform SLAM. However, these algorithms face common challenges are that of computational complexity, wrong loop closure detection and failure to localize correctly when robot loses state (kidnapped robot problem). In this work, we utilize Wi-Fi signal strength sensing to aid the SLAM process in indoor environments and address the challenges mentioned above.
Published in: 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
Date of Conference: 18-21 April 2017
Date Added to IEEE Xplore: 12 June 2017
ISBN Information:
Conference Location: Pittsburgh, PA, USA