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

基于深度强化学习和多模型自适应估计的多传感器融合同步定位映射 [J].

• 影响指数: 3.847
• 发表时间: 2023 Dec 21
• 来源期刊: Sensors (Basel)
• DOI: 10.3390/s24010048
• PMID: 38202911
• 文章类型: Journal Article
• 中科院分区: Q2;Q3;Q1
• 作者列表: Wong CC,Feng HM,Kuo KL

关键词: deep reinforcement learning (DRL); multi-model adaptive estimation (MMAE); sensor fusion; simultaneous localization and mapping (SLAM)

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.

摘要

在这项研究中,我们设计了一种基于深度强化学习（DRL）机制和多模型自适应估计（MMAE）的多传感器融合技术，用于同时定位和映射（SLAM）。基于LiDAR的点对点迭代最近点（PLICP）和基于RGB-D相机的ORBSLAM2方法用于估计移动机器人的定位。将残差值异常检测与基于近端策略优化（PPO）的DRL模型相结合，以实现不同定位算法之间权重的最佳调整。利用Gazebo模拟器建立了两种室内模拟环境,验证了多模型自适应估计定位性能,这是在本文中使用的。本研究中提出的方法的实验结果证实，它可以有效地融合来自多个传感器的定位信息，并使移动机器人获得比传统PLICP和ORBSLAM2更高的定位精度。还发现，该方法提高了移动机器人在复杂环境中的定位稳定性。