SLAM in Environment with Glass Using Degree of Polarization from Polarization Camera and Depth Information from LRF

Abstract

This paper proposes a method to improve the accuracy of SLAM in environments with glass by combining LRF and a polarization camera to detect a wide range of glass. Glass confidence of each point detected by the LRF is calculated using the polarization from the polarization camera. The polarization camera acquires the intensity of light passing through the polarizers in four directions. The degree of polarization is calculated from the light intensity of four directions, and is used as the glass confidence. Every time the robot moves, the map and the glass confidence are updated. The robot’s position is estimated using the generated map. Accuracy of the map is improved by considering the glass probability. Improved accuracy of the map also improves the accuracy of self-localization. The accuracy of glass detection was confirmed by the experimental results. The AUC of glass detection was 0.942. As a result, the proposed method was able to produce a more accurate map in the glass area.