Indoor robot navigation using a POMDP based on WiFi and ultrasound observations

This paper presents a robot navigation system for indoor environments using a partially observable Markov decision process (POMDP) based on WiFi signal strength and ultrasound observations. The paper represents the first one in using WiFi sensor readings as an observation in a POMDP. We present an algorithm based on an EM-SLAM that we called WSLAM (Wifi simultaneous localization and mapping) that is able to learn the observation and transition matrix in autonomous mode. With this algorithm we obtain a minimum calibration effort. We demonstrate that this system is useful to navigate in indoor environments with a real robot. Some experimental results are shown. Finally, the conclusions and future works are presented.