Enhanced PCA-based localization using depth maps with missing data

In this paper a new method for a global self-localization of mobile robots based on a PCA positioning sensor to operate in unstructured environments is proposed and experimentally validated. Unlike many existing systems that use RGB signals to capture information from the environment, in this work a 2D depth sensor is used, allowing the self-localization to be performed under different illumination conditions. However, depth sensors provide measurements corrupted with missing data, due to limitations on the support physic principles exploited (e.g. light that illuminates surfaces with diffuse reflection or wave fading), which severely degrades the performance of the estimation techniques and limits its use. The main goal of this paper is to present a self-localization system for mobile robots based on a PCA positioning sensor that relies on corrupted depth measurements and the corresponding experimental validation. The proposed method allows for the implementation of a global self-localization system for indoor environments with higher accuracy, that provide a Gaussian estimated position error and work in any illumination conditions.