ASLAM-FD: a multi-sensor adaptive collaborative fusion SLAM framework based on degradation detection and deep reinforcement learning

Abstract Aiming at the difficulty in ensuring accuracy and performance of existing simultaneous localization and mapping (SLAM) technology based on multi-sensor fusion in complex dynamic environments, a multi-sensor adaptive fusion SLAM framework based on degradation detection and deep reinforcement learning (ASLAM-FD) is proposed. This framework can achieve adaptive collaborative precise adjustment of fusion weights (FWs) based on the real-time self-degradation states of each sensor and relative degradation states quantified, and can adapt to different tightly coupled SLAM algorithms based on FWs. In addition, within this framework, the continuous quantification models for the degradation states of internal/external sensors with certain versatility (we refer to them as external degeneracy compute model and internal degeneracy compute model) are proposed. These quantification models can achieve continuous quantification of degradation states of various common internal/external sensors. Based on the above sensor degradation state quantification models, this paper further proposes using a deep reinforcement learning network based on long short term memory to achieve adaptive collaborative adjustment of FWs based on sensor degradation states. In the experimental section, we adapted the proposed ASLAM-FD to different multi-sensor fusion SLAM algorithms on multiple datasets, and compared it with multiple advanced fusion SLAM algorithms. We found that adapting ASLAM-FD can effectively improve the accuracy and performance of fusion SLAM in complex dynamic environments.