  Accurate state estimation is a fundamental problem for autonomous robots. To
achieve locally accurate and globally drift-free state estimation, multiple
sensors with complementary properties are usually fused together. Local sensors
(camera, IMU, LiDAR, etc) provide precise pose within a small region, while
global sensors (GPS, magnetometer, barometer, etc) supply noisy but globally
drift-free localization in a large-scale environment. In this paper, we propose
a sensor fusion framework to fuse local states with global sensors, which
achieves locally accurate and globally drift-free pose estimation. Local
estimations, produced by existing VO/VIO approaches, are fused with global
sensors in a pose graph optimization. Within the graph optimization, local
estimations are aligned into a global coordinate. Meanwhile, the accumulated
drifts are eliminated. We evaluate the performance of our system on public
datasets and with real-world experiments. Results are compared against other
state-of-the-art algorithms. We highlight that our system is a general
framework, which can easily fuse various global sensors in a unified pose graph
optimization. Our implementations are open
source\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.

Computer Vision and Pattern Recognition