An algorithm for real-time restructuring of a ranging-based localization network
This work addresses localization challenges for robots in environments with limited reliable systems, though it appears incremental in its approach.
The paper tackles the problem of improving robot localization accuracy in harsh environments by introducing an adaptive algorithm that restructures a ranging-based network in real-time, achieving enhanced localization quality through experimental validation with one mobile and four fixed anchors.
This paper presents a method to improve the localization accuracy of robots operating in a range-based localization network. The method is favorable especially when the robots operate in harsh environments where the access to a robust and reliable localization system is limited. A state estimator is used for a six degree of freedom object using inertial sensors as well as an Ultra-wideband (UWB) range measurement sensor. The estimator is incorporated into an adaptive algorithm, improving the localization quality of an agent by using a mobile UWB ranging sensor, where the mobile anchor moves to improve localization quality. The algorithm reconstructs localization network in real-time to minimize the determinant of the covariance matrix in the sense of least square error. Finally, the proposed algorithm is experimentally validated in a network consisting of one mobile and four fixed anchors.