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Accurate state estimation is a fundamental problem for autonomous robots. Toachieve locally accurate and globally drift-free state estimation, multiplesensors with complementary properties are usually fused together. Local sensors(camera, IMU, LiDAR, etc) provide precise pose within a small region, whileglobal sensors (GPS, magnetometer, barometer, etc) supply noisy but globallydrift-free localization in a large-scale environment. In this paper, we proposea sensor fusion framework to fuse local states with global sensors, whichachieves locally accurate and globally drift-free pose estimation. Localestimations, produced by existing VO/VIO approaches, are fused with globalsensors in a pose graph optimization. Within the graph optimization, localestimations are aligned into a global coordinate. Meanwhile, the accumulateddrifts are eliminated. We evaluate the performance of our system on publicdatasets and with real-world experiments. Results are compared against otherstate-of-the-art algorithms. We highlight that our system is a generalframework, which can easily fuse various global sensors in a unified pose graphoptimization. Our implementations are opensource\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.

A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors