Indoor Mobile Robot Localization and Mapping Based on Ambient Magnetic Fields and Aiding Radio Sources

In robotics, the problem of concurrently addressing the localization and mapping is well defined as simultaneous localization and mapping (SLAM) problem. Since the SLAM procedure is usually recursive, maintaining a certain error bound on the current position estimate is a critical issue. However, when the robot is kidnapped (i.e., the robot is moved by an intentional or unintentional user) or suffers from locomotion failure (due to large slip and falling), the robot will inevitably lose its current position. In this case, immediate recovery of the robot position is essential for seamless operation. In this paper, we present a method of solving both SLAM and relocation problems by employing ambient magnetic and radio measurements. The proposed SLAM is realized in the Rao-Blackwellized particle filter- and grid-based SLAM frameworks, where we exploit the local heading corrections from the magnetic measurements. For the relocation, we design the location signatures using the magnetic and radio measurements, and examine each of the Monte Carlo localization-based and multilayer perceptron-based relocation methods with real-world data. We implement the proposed SLAM and relocation algorithms in an embedded system and verify the feasibility of the proposed methods as an online robot navigation system.