A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors
This work addresses the challenge of accurate state estimation for autonomous robots, though it is incremental as it builds upon existing VO/VIO methods and sensor fusion techniques.
The authors tackled the problem of achieving locally accurate and globally drift-free pose estimation for autonomous robots by proposing a sensor fusion framework that integrates local sensors (e.g., camera, IMU) with global sensors (e.g., GPS) using pose graph optimization, resulting in improved performance compared to state-of-the-art algorithms on public datasets and real-world experiments.
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}.