Graph Optimization Approach to Localization with IMU and Ultra-Wideband Measurements
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An ultra-wideband (UWB) aided localization system is presented. Existing filter-based methods using UWB need kinetic model which is generally hard to obtain because of the complex structure of robots. This paper proposes a graph optimization approach to localization with inertial measurement unit (IMU) and UWB measurements which converts the localization problem into an optimization problem on Lie-Manifold, so that kinetic model can be avoided. This method makes full use of measurement data to localize a robot with acceptable computational complexity. It is achieved by minimizing the trajectory error based on several constrained equations arising from different types of sensor measurements. Our first contribution is graph realization in Manifold instead of Euclidean space for UWB-based systems, and new type of edge is defined and created in order to apply exiting graph optimization tool. Our second contribution is online approach that enables this approach to robots with ultra-low power processor. Experiments under a variety of scenarios verify the stability of this method and demonstrate that the algorithm achieves a much higher localization accuracy than the filter-based methods.
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