Monte Carlo localization in dense multipath environments using UWB ranging

For most outdoor applications, systems such as GPS provide users with accurate position estimates. However, reliable range-based localization using radio signals in indoor or urban environments can be a problem due to multipath fading and line-of-sight (LOS) blockage. The measurement bias introduced by these delays causes significant localization error, even when using additional sensors such as an inertial measurement unit (IMU) to perform outlier rejection. We describe an algorithm for accurate indoor localization of a sensor in a network of known beacons. The sensor measures the range to the beacons using an Ultra-Wideband (UWB) signal and uses statistical inference to infer and correct for the bias due to LOS blockage in the range measurements. We show that a particle filter can be used to estimate the joint distribution over both pose and beacon biases. We use the particle filter estimation technique specifically to capture the non-linearity of transitions in the beacon bias as the sensor moves. Results using real-world and simulated data are presented.