In the race to improve autonomous systems, researchers in China have unveiled a new algorithm that accelerates navigation while maintaining precision. Called Optimized iSAM-Factor Graph Optimization (OiSAM-FGO), the method halves processing times compared with leading benchmarks yet delivers accuracy equal to state-of-the-art solutions.

Traditional navigation tools face persistent trade-offs. Global Navigation Satellite Systems (GNSS) struggle in cities, while Inertial Navigation Systems (INS) drift over time. Fusion algorithms like the Extended Kalman Filter help but fail to capture nonlinear dynamics. Factor Graph Optimization (FGO) emerged as a global solution but proved too computationally demanding for embedded devices.

The OiSAM-FGO framework, developed by the Institute of Microele

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