{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T02:47:00Z","timestamp":1774061220582,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology (MOST) of the Republic of China","award":["MOST 111-2221-E-032-030"],"award-info":[{"award-number":["MOST 111-2221-E-032-030"]}]},{"name":"Science and Technology (MOST) of the Republic of China","award":["NSTC 112-2221-E-032-035-MY2"],"award-info":[{"award-number":["NSTC 112-2221-E-032-035-MY2"]}]},{"name":"Science and Technology (MOST) of the Republic of China","award":["NSTC 112-2221-E-507-007-MY2"],"award-info":[{"award-number":["NSTC 112-2221-E-507-007-MY2"]}]},{"name":"National Science and Technology Council (NSTC) of the Republic of China","award":["MOST 111-2221-E-032-030"],"award-info":[{"award-number":["MOST 111-2221-E-032-030"]}]},{"name":"National Science and Technology Council (NSTC) of the Republic of China","award":["NSTC 112-2221-E-032-035-MY2"],"award-info":[{"award-number":["NSTC 112-2221-E-032-035-MY2"]}]},{"name":"National Science and Technology Council (NSTC) of the Republic of China","award":["NSTC 112-2221-E-507-007-MY2"],"award-info":[{"award-number":["NSTC 112-2221-E-507-007-MY2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s24010048","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T05:36:12Z","timestamp":1703136972000},"page":"48","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-Sensor Fusion Simultaneous Localization Mapping Based on Deep Reinforcement Learning and Multi-Model Adaptive Estimation"],"prefix":"10.3390","volume":"24","author":[{"given":"Ching-Chang","family":"Wong","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 25137, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6498-7006","authenticated-orcid":false,"given":"Hsuan-Ming","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Quemoy University, Kinmen County 89250, Taiwan"}]},{"given":"Kun-Lung","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 25137, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"ref_1","unstructured":"Gerkey, B. 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