A Novel Cooperative Positioning Algorithm Based on High-Precision IMU Preintegration and Adaptive Sliding Observation Optimization

Environments with dense obstacles that cause multipath propagation of ranging signals present a critical challenge that needs to be urgently addressed for multiple agents cooperative system to achieve high-precision positioning. Aiming to address the problems of imprecise models in typical inertial measurement unit (IMU) preintegration-based graph optimization algorithms and the non-Gaussian distribution of ranging sensor noise in multiple agents cooperative system, this article proposes a novel cooperative positioning algorithm based on high-precision IMU preintegration and adaptive sliding observation optimization (HIPaASOO). First, a leader–follower cooperative positioning model is constructed, accounting for the spatial offset between micro-electromechanical system (MEMS) and ultrawideband (UWB). Then, a high-precision IMU preintegration model incorporating Coriolis force and centripetal force is derived. Finally, an adaptive sliding observation optimization model is designed to enhance robustness against ranging outliers. Multiple sets of simulations and multiple unmanned ground vehicles (UGVs) cooperative experiments have been conducted, validating the exceptional performance of the proposed algorithm. In multiple UGVs experiments conducted in obstacle-dense environments, the proposed algorithm achieved reductions in positioning error of 16.2% and 7.8% compared to algorithms based on rough IMU preintegration model and improved Earth-rotation-aware IMU preintegration model, respectively.