A Biologically Inspired Global Localization System for Mobile Robots Using LiDAR Sensor
This addresses the challenge of reliable indoor localization for mobile robots, though it appears incremental by building on existing biological inspiration and methods.
The paper tackled the global localization problem for mobile robots in indoor environments by developing a biologically-inspired system using a LiDAR sensor, hippocampal model, and landmark-based re-localization, achieving competitive accuracy compared to Monte Carlo Localization.
Localization in the environment is an essential navigational capability for animals and mobile robots. In the indoor environment, the global localization problem remains challenging to be perfectly solved with probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, it is intriguing and promising to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based re-localization approach. The experiment results show that the proposed method is competitive to Monte Carlo Localization, and the results demonstrate the high accuracy, applicability, and reliability of the proposed biologically-inspired localization system in different localization scenarios.