  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 for
mobile robots such as no entry zone can be predicted if localization has
succeeded since relative pose of them can be known. However, this prediction is
failed when localization does not work. Uncertainty of pose estimate must be
considered for robustly using the map information. In addition, robots have
external sensors and environment information can be recognized using the
sensors. This on-line recognition of course contains uncertainty; however, it
has to be fused with the map information for robust environment recognition
since the map also contains uncertainty owing to over time. SLAMER can
simultaneously cope 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.
