Building a Self-Adjusted Map for Mobile Robot Navigation
An accurate map is essential to autonomous mobile robots. Two of the most popular map types are Occupancy Grid Map (OGM) and Topological Map (TM). However, the OGM is inefficient since it requires a large amount of memory to represent the environment. The TM, on the other hand, is difficult to apply to accurate navigation and location because it lacks environmental information. In this paper, a self-adjusted mapping method is proposed with integrating the two map types. It navigates the robot more efficiently than an OGM, and more accurately than a TM. With this new map type, a reliable path could be obtained in real-time. Simulation experiments validate its efficiency and reliability.