Principal Component Analysis (PCA) and Hough Transform as Tool for Simultaneous Localization and Mapping (SLAM) with Sparse and Noisy Sensors

Abstract

This work proposes a method of handling the difficulties generated by sparse and noisy sensorial output from a small quantity of ultrasonic sensors in order to develop a low cost SLAM system. A pre processing step of detecting faulty sensors was implemented by applying PCA on the available data in order to extract more reliable baseline features through the Hough Transform. Furthermore, we analyze the influence of odometry errors and failures in the localization of a differential driven mobile robot. This method is suitable for indoor and orthogonal shaped environments, especially for medium and short term tasks, such as exploration, rescue and inspection. The experimental results demonstrate the accuracy and robustness to noise and sensorial failures.