Lvio-Fusion: A Self-adaptive Multi-sensor Fusion SLAM Framework Using Actor-critic Method
This addresses sensor fusion challenges for mobile robots in dynamic environments, representing an incremental improvement with a novel adaptive weighting mechanism.
The paper tackles the problem of fusing measurements from multiple sensors for state estimation in mobile robots, proposing Lvio-Fusion, a framework that uses graph optimization and an actor-critic method to adaptively adjust sensor weights, achieving high accuracy and robustness in urban traffic scenes.
State estimation with sensors is essential for mobile robots. Due to different performance of sensors in different environments, how to fuse measurements of various sensors is a problem. In this paper, we propose a tightly coupled multi-sensor fusion framework, Lvio-Fusion, which fuses stereo camera, Lidar, IMU, and GPS based on the graph optimization. Especially for urban traffic scenes, we introduce a segmented global pose graph optimization with GPS and loop-closure, which can eliminate accumulated drifts. Additionally, we creatively use a actor-critic method in reinforcement learning to adaptively adjust sensors' weight. After training, actor-critic agent can provide the system better and dynamic sensors' weight. We evaluate the performance of our system on public datasets and compare it with other state-of-the-art methods, which shows that the proposed method achieves high estimation accuracy and robustness to various environments. And our implementations are open source and highly scalable.