DV-SLAM (Dual-Sensor-Based Vector-Field SLAM) and Observability Analysis

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.