SmartSLAM: localization and mapping across multi environments

Abstract

In the absence of absolute localization tools such as GPS, a robot can still successfully navigate by conducting simultaneous localization and mapping (SLAM). All SLAM algorithms to date can only be applied in one environment at a time. In this paper we propose to extend SLAM to multi-environments. In SmartSLAM, the robot first classifies its entourage using environment recognition code and then performs SLAM using landmarks that are appropriate for its surrounding milieu. One thousand images of various indoor and outdoor environments were collected and used as training data for a three-layered feedforward backpropagation neural network. This neural network was then tested on two sets of query images of indoor environments and another two sets of outdoor environments, yielding 83% and 95% correct classification rates for the indoor images and 80% and 79% success rates for the outdoor images.