Simultaneous localization and mapping (SLAM) has a wide range for applications in mobile robotics. Lightweight and inexpensive vision sensors have been widely used for localization in GPS-denied or weak GPS environments. Mobile robots not only estimate their pose, but also correct their position according to the environment, so a proper mathematical model is required to obtain the state of robots in their circumstances. Usually, filter-based SLAM/VO regards the model as a Gaussian distribution in the mapping thread, which deals with the complicated relationship between mean and covariance. The covariance in SLAM or VO represents the uncertainty of map points. Therefore, the methods, such as probability theory and information theory play a significant role in estimating the uncertainty. In this paper, we combine information theory with classical visual odometry (SVO) and take Jensen-Shannon divergence (JS divergence) instead of Kullback-Leibler divergence (KL divergence) to estimate the uncertainty of depth. A more suitable methodology for SVO is that explores to improve the accuracy and robustness of mobile devices in unknown environments. Meanwhile, this paper aims to efficiently utilize small portability for location and provide a priori knowledge of the latter application scenario. Therefore, combined with SVO, JS divergence is implemented, which has been realized. It not only has the property of accurate distinction of outliers, but also converges the inliers quickly. Simultaneously, the results show, under the same computational simulation, that SVO combined with JS divergence can more accurately locate its state in the environment than the combination with KL divergence.

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BACKGROUND A standardized method for identifying medical laboratory observations, such as Logical Observation Identifiers Names and Codes (LOINC), is critical for creating accurate and effective public data models. However, such standards are not being used effectively. Standardized mapping facilitates consistency in medical terminologies and data sharing in multicenter treatment. OBJECTIVE To address the problem of standardizing laboratory test terminologies, a deep learning–based high-precision end-to-end terminology standardization matching system was developed to map laboratory test terms (LTTs) to LOINC. METHODS We manually constructed a laboratory test terminology mapping dataset containing 15,349 data items extracted from the information system of the Shengjing Hospital of China Medical University and matched 2,375 LOINC. We developed Attribute-wised Graph Attention Siamese Network (AGASN), a deep learning–based high-precision laboratory test terminology mapping model, to separately extract LTT features and LOINC term features and calculate the matching rate. We designed an attribute pooling mechanism to convert terminology strings to attribute sequences. Moreover, we developed a graph attention model based on attribute relations, which increased the interpretability of the proposed model. The problem of inconsistency in training and testing objectives was solved by improving the training objectives of the model. RESULTS The proposed a novel deep learning model achieved an accuracy of 82.33% ± 0.6% on the test dataset where the LOINC were visible, corresponding to a 10.9% improvement compared with that obtained using a random forest classifier. Furthermore, the proposed system achieved an accuracy of 63.14% ± 0.2% on the test dataset where the LOINC were invisible, constituting a 10.0% improvement compared with that obtained using SimCSE. Manual validation of the system performance showed accuracies of 82.33% and 70.66% on labeled and unlabeled datasets, respectively. Finally, we constructed a visual attribute relational strength network using an attribute graph attention model. CONCLUSIONS Herein, a Chinese laboratory test terminology mapping dataset was created and a deep learning system for the standardized mapping of LTTs was proposed. The results demonstrate that the proposed system can map LTTs to LOINC with high accuracy.