SLAMER: Simultaneous Localization and Map-Assisted Environment Recognition

This paper presents a simultaneous localization and map-assisted environment recognition (SLAMER) method. Mobile robots usually have an environment map and environment information can be assigned to the map. Important information such as no entry zone can be predicted from the map if localization has succeeded. However, this prediction is failed when localization does not work. Uncertainty of pose estimate must be considered for robust-map-based environ-mental object prediction. Robots also have external sensors and can recognize environmental object; however, sensor-based recognition of course contain uncertainty. SLAMER fuses map-based prediction and sensor-based recognition while coping with these uncertainties and achieves accurate localization and environment recognition. In this paper, we demonstrate LiDAR-based implementation of SLAMER in two cases. In the first case, we use the SemanticKITTI dataset and show that SLAMER achieves accurate estimate more than traditional methods. In the second case, we use an indoor mobile robot and show that unmeasurable environmental objects such as open doors and no entry lines can be recognized.