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State estimation with sensors is essential for mobile robots. Due todifferent performance of sensors in different environments, how to fusemeasurements of various sensors is a problem. In this paper, we propose atightly coupled multi-sensor fusion framework, Lvio-Fusion, which fuses stereocamera, Lidar, IMU, and GPS based on the graph optimization. Especially forurban traffic scenes, we introduce a segmented global pose graph optimizationwith GPS and loop-closure, which can eliminate accumulated drifts.Additionally, we creatively use a actor-critic method in reinforcement learningto adaptively adjust sensors' weight. After training, actor-critic agent canprovide the system better and dynamic sensors' weight. We evaluate theperformance of our system on public datasets and compare it with otherstate-of-the-art methods, which shows that the proposed method achieves highestimation accuracy and robustness to various environments. And ourimplementations are open source and highly scalable.

Lvio-Fusion: A Self-adaptive Multi-sensor Fusion SLAM Framework Using Actor-critic Method