This paper proposes a new Simultaneous Localization and Mapping (SLAM) method on the basis of graph-based optimization through the combination of the Light Detection and Ranging (LiDAR), RGB-D camera, encoder and Inertial Measurement Unit (IMU). It can conduct joint positioning of four sensors by taking advantaging of the unscented Kalman filter (UKF) to design the related strategy of the 2D LiDAR point cloud and RGB-D camera point cloud. 3D LiDAR point cloud information generated by the RGB-D camera under the 2D LiDAR has been added into the new SLAM method in the sequential registration stage, and it can match the 2D LiDAR point cloud and the 3D RGB-D point cloud by using the method of the Correlation Scan Matching (CSM); In the loop closure detection stage, this method can further verify the accuracy of the loop closure after the 2D LiDAR matching by describing 3D point cloud.Additionally, this new SLAM method has been verified feasibility and availability through the processes of theoretical derivation, simulation experiment and physical verification. As a result, the experiment shows that the multi-sensor SLAM framework designed has a good mapping effect, high precision and accuracy.
Recently, many deep models have been proposed in different fields, such as image classification, object detection, and speech recognition. However, most of these architectures require a large amount of training data and employ random initialization. In this paper, we propose to stack feature learning modules for the design of deep architectures. Specifically, marginal Fisher analysis (MFA) is stacked layer-by-layer for the initialization and we call the constructed deep architecture marginal deep architecture (MDA). When implementing the MDA, the weight matrices of MFA are updated layer-by-layer, which is a supervised pretraining method and does not need a large scale of data. In addition, several deep learning techniques are applied to this architecture, such as backpropagation, dropout, and denoising, to fine-tune the model. We have compared MDA with some feature learning and deep learning models on several practical applications, such as handwritten digits recognition, speech recognition, historical document understanding, and action recognition. The extensive experiments show that the performance of MDA is better than not only shallow feature learning models but also related deep learning models in these tasks. INDEX TERMS Deep architectures, feature learning, marginal Fisher analysis, marginal deep architecture.
In most profiles, children who take asthma controller medication do so regularly. Follow-up physician office visits after an ED encounter or hospitalization are observed at a low rate across all states. Finally, all states have a proportion of children who have uncontrolled asthma, meaning they do not take controller medication while they have severe outcomes.
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