Mag-ODO: Motion speed estimation for indoor robots based on dual magnetometers

In indoor environments, accurate speed estimation is crucial for providing continuous and reliable position of mobile robots. However, conventional odometry methods may suffer from performance degradation, particularly in scenarios involving wheel odometry slipping or visual odometry blurring. In this study, we propose Mag-ODO, a novel motion speed estimation method based on dual magnetometers aimed at enhancing the robustness of indoor robot positioning systems. The dual magnetometers are mounted at the front and back of the robot, and the speed is estimated by matching the magnetic-filed waveforms sampled from the magnetometers. We employ the dynamic time warping (DTW) algorithm to implement waveform matching and use both the magnetic field strengths and the changing trend of the magnetic field strengths as the matching cost function, effectively reducing the matching error. Mag-ODO has two key advantages: immunity to magnetometer bias and transient disappearance of magnetic field gradients within a matching time window does not affect speed estimation accuracy. Test results show that Mag-ODO performs similarly to Wheel-ODO in magnetic-rich environments (RMSE < 0.06 m/s) and comparable dead reckoning (DR) performance with inertial navigation systems (INS) in both straight and curved environments.