SLAMER: Simultaneous Localization and Map-Assisted Environment Recognition

TL;DR

SLAMER is a method that combines localization and environment recognition by fusing uncertain map and sensor data, demonstrated with LiDAR data in outdoor and indoor scenarios to improve accuracy.

Contribution

This paper introduces SLAMER, a novel approach that simultaneously handles uncertainties in localization and environment recognition for mobile robots.

Findings

SLAMER outperforms traditional methods in accuracy on SemanticKITTI dataset.

SLAMER successfully recognizes unmeasurable environmental features like doors and no-entry lines.

Demonstrated effectiveness in both outdoor and indoor environments.

Abstract

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.