A General Optimisation-Based Framework for Global Pose Estimation With Multiple Sensors

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 (inertial measurement unit), LiDAR, etc.) provide precise poses within a small region, whereas global sensors (GPS (global positioning system), magnetometer, barometer, etc.) supply noisy but globally drift-free localisation 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 visual odometry/visual-inertial odometry (VO/VIO) approaches, are fused with global sensors in a pose graph optimisation. Within the graph optimisation, local estimations are aligned into a global coordinate. Meanwhile, the accumulated drifts are eliminated. We evaluated the performance of our system on public datasets and with real-world experiments. The results are compared with those of 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 optimisation.