Robust Localization Using Map Selection from Multiple Maps Based on the Relative Pose Difference of Map and Interoceptive Sensor

Autonomous mobile robots are expected to demon- strate a high degree of adaptability, enabling effective opera- tion across diverse environments. Robust localization is a key requirement for achieving such autonomous mobility. Typically, localization methods using sensors such as cameras and LiDAR construct a map in advance from features of the driving environment. The robot then estimates its pose by matching the currently observed features to the pre-prepared map. However, environmental changes create discrepancies between the current environment and the pre-prepared map, leading to localization failure. This paper presents a map selection method for robust localization, which selects a map that reflects the current environment from multiple pre-prepared maps constructed under different environmental conditions. Although existing methods are limited to a single sensor, the proposed method can be applied to different types of sensors in a unified manner by handling sensor information at the pose information layer. To achieve this, the method utilizes the relative pose difference between the interoceptive sensor and the map, which is less susceptible to environmental changes, thereby enabling appro- priate map selection under varying environmental conditions. The experiment was conducted using a robot equipped with a stereo camera in an environment with four conditions. The results showed maximum localization errors of 0.10–0.24 m and mean errors of 0.03–0.04 m, demonstrating robust localization through the selection of an appropriate map that reflects the current conditions.