Adapted Error Map Based Mobile Robot UWB Indoor Positioning

Abstract

Ultrawideband (UWB) has gained a lot of attention in indoor positioning due to its high time resolution, whereas its accuracy could be easily affected by the indoor environment. A UWB error map could represent the distribution of the positioning errors in a static indoor space and help to improve the accuracy of indoor positioning. However, the building of the error map needs much work. In this article, we propose a UWB error map building method, which adapts the distribution of positioning error measurement points to a rough positioning error distribution. An adapted error map based particle filter (AEMBPF), which adopts the error map in the initialization and weight update of the particle set, is then proposed to improve the accuracy of UWB positioning. The experimental results have shown a nearly 50% reduction in the number of measurement points and better positioning accuracy with faster convergence speed.