Multi-Sensor Fusion Simultaneous Localization Mapping Based on Deep Reinforcement Learning and Multi-Model Adaptive Estimation

doi:10.3390/s24010048

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題名:	Multi-Sensor Fusion Simultaneous Localization Mapping Based on Deep Reinforcement Learning and Multi-Model Adaptive Estimation
作者:	Ching-Chang Wong, Hsuan-Ming Feng, Kun-Lung Kuo
關鍵詞:	simultaneous localization and mapping (SLAM);deep reinforcement learning (DRL);multi-model adaptive estimation (MMAE);sensor fusion
日期:	2023-12-21
上傳時間:	2024-07-30 12:06:25 (UTC+8)
出版者:	MDPI
摘要:	In this study, we designed a multi-sensor fusion technique based on deep reinforcement learning (DRL) mechanisms and multi-model adaptive estimation (MMAE) for simultaneous localization and mapping (SLAM). The LiDAR-based point-to-line iterative closest point (PLICP) and RGB-D camera-based ORBSLAM2 methods were utilized to estimate the localization of mobile robots. The residual value anomaly detection was combined with the Proximal Policy Optimization (PPO)-based DRL model to accomplish the optimal adjustment of weights among different localization algorithms. Two kinds of indoor simulation environments were established by using the Gazebo simulator to validate the multi-model adaptive estimation localization performance, which is used in this paper. The experimental results of the proposed method in this study confirmed that it can effectively fuse the localization information from multiple sensors and enable mobile robots to obtain higher localization accuracy than the traditional PLICP and ORBSLAM2. It was also found that the proposed method increases the localization stability of mobile robots in complex environments.
關聯:	Sensors 24(1), 48
DOI:	10.3390/s24010048
顯示於類別:	[電機工程學系暨研究所] 期刊論文

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