In this paper, the observability of the conventional vector field simultaneous localization and mapping (SLAM) is examined by using the Fisher information matrix (FIM). If a mobile robot integrates sensor measurements while moving with a fixed heading, the measurements will be ambiguous because its measurement model is based on bilinear interpolation. To resolve the ambiguity, the authors proposed the novel dual-sensor-based vector-field SLAM (DV-SLAM), which is fully observable by using a mobile robot equipped with two sensors in a specific location to measure vector field signals. By examining its FIM, the condition is derived for the proposed DV-SLAM to be fully observable regardless of how the robot moves. The proposed DV-SLAM is implemented based on the Rao-Blackwellized particle filter with Earth's magnetic field sensors. Simulation and experimental results demonstrate that the proposed dual-sensor-based approach greatly improves the performance of the vector-field SLAM compared with the conventional approach.Index Terms-Dual sensor, Rao-Blackwellized particle filter (RBPF), simultaneous localization and mapping (SLAM), vector-field SLAM.
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.
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