A General Optimisation‐Based Framework for Global Pose Estimation With Multiple Sensors
Tong Qin, Shaozu Cao, Jie Pan, Shaojie Shen
Abstract
ABSTRACT 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.