Abstract
Odometry is commonly used in localization applications especially with wheeled platforms since encoders are readily available. It is often used by itself or fused with other sensor data to obtain a better estimate. However, its limitation is its exclusivity to wheeled platforms whereas it is often desired to have similar encoder odometry options on other systems. Given that WiFi is ubiquitous in most commercial and industrial areas, in this paper, a method is proposed for obtaining odometry from WiFi scans for position estimation. The method is not constrained to wheel robots such as the case for wheeled odometry and does not rely on the traditional fingerprinting method. The proposed method involves training a neural network model to predict the distance moved based on features extracted from WiFi scans in the environment. These distances moved are then summed up to obtain the trajectory. Experiments are conducted and the methods are evaluated based on Root Mean Square Error (RMSE). Experimental results showed that the proposed method is able to achieve an RMSE of at most 8.39m for the various test cases.