ArticleSensors (Basel, Switzerland)2023
Multi-Sensor Fusion Simultaneous Localization Mapping Based on Deep Reinforcement Learning and Multi-Model Adaptive Estimation.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Optimizing Autonomous Vehicle Performance Using Improved Proximal Policy Optimization.Sensors (Basel, Switzerland) · 2025Article
- Exploring the Unseen: A Survey of Multi-Sensor Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles.Sensors (Basel, Switzerland) · 2025Review
- Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception.Sensors (Basel, Switzerland) · 2024Review
- Multimodal Image-Based Indoor Localization with Machine Learning-A Systematic Review.Sensors (Basel, Switzerland) · 2024Review
- VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation.Sensors (Basel, Switzerland) · 2024Article
- A Robotics Experimental Design Method Based on PDCA: A Case Study of Wall-Following Robots.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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