A Robust Position Estimation Algorithm under Unusual Large Range Errors This paper provides a robust fusion algorithm for accurate position estimation under uncertain large errors in range measurements. Many researchers have studied UWB, IMU, odometry integration algorithms for estimating position, velocity, attitude and IMU biases. However, the fused results of these conventional algorithms are likely to be affected by the unusual ranging measurement errors. Therefore, in order to improve the positioning accuracy under the large errors of the UWB range, a robust fusion algorithm is required. In this paper, instead of using the range directly, the estimated range that the errors is reduced is used. Reducing the errors is possible by using the odometry velocity that is relatively accurate and is independent of range measurement. The robustness and accuracy of the proposed algorithm is verified by a mobile robot with realtime positioning and trajectory control under large range errors which are occurred randomly.

Robot Localization via Odometryassisted Ultrawideband Ranging with Stochastic Guarantees We consider the problem of accurate and highrate selflocalization for a mobile robot. We adaptively combine the speed information acquired by proprioceptive sensors with intermittent positioning samples acquired via ultrawideband (UWB) radios. These are triggered only if and when needed to reduce the positioning uncertainty, itself modeled by a probabilistic cost function. Our formulation is agnostic w.r.t. the source of uncertainty and enables an intuitive specification of user navigation requirements along with stochastic guarantees on the system operation. Experimental results in simulation and with a real platform show that our approach i) meets these guarantees in practice ii) achieves the same accuracy of a fixed periodic sampling but with significantly higher scalability and lower energy consumption iii) is resilient to errors in UWB estimates, enabling the use of lowaccuracy ranging schemes which further improve these two performance metrics.