Loop closure detection of visual SLAM based on variational autoencoder.

Loop closure detection is an important module for simultaneous localization and mapping (SLAM). Correct detection of loops can reduce the cumulative drift in positioning. Because traditional detection methods rely on handicraft features, false positive detections can occur when the environment changes, resulting in incorrect estimates and an inability to obtain accurate maps. In this research paper, a loop closure detection method based on a variational autoencoder (VAE) is proposed. It is intended to be used as a feature extractor to extract image features through neural networks to replace the handicraft features used in traditional methods. This method extracts a low-dimensional vector as the representation of the image. At the same time, the attention mechanism is added to the network and constraints are added to improve the loss function for better image representation. In the back-end feature matching process, geometric checking is used to filter out the wrong matching for the false positive problem. Finally, through numerical experiments, the proposed method is demonstrated to have a better precision-recall curve than the traditional method of the bag-of-words model and other deep learning methods and is highly robust to environmental changes. In addition, experiments on datasets from three different scenarios also demonstrate that the method can be applied in real-world scenarios and that it has a good performance.

Effect of resistance training on heart rate variability of anxious female college students.

Introduction: Female college students are a group with high incidence of anxiety, and anxiety will lead to the disorder of autonomic nervous system (ANS), which will adversely affect their study and life. Resistance training plays a positive role in improving anxiety, but there is little evidence on whether resistance training can improve ANS of anxious female college students. Heart rate variability (HRV) has gained widespread acceptance in assessing ANS modulation. Therefore, the objective of this study aimed to investigate the effects of resistance training on heart rate variability (HRV) in anxious female college student. Methods: A randomized controlled study of resistance training intervention was conducted in 27 anxious female college students that assigned randomly into an intervention group (n = 14) and a control group (n = 13). The intervention group was intervened by cluster training for 8 weeks. Self-rating anxiety scale (SAS) was used. ANS is evaluated by short-term HRV. Muscle strength was assessed by 1 RM indirect method. Independent-sample t-test was used to test post-test-pre-test scores between the intervention and control groups. Results: After the intervention, SAS score of the intervention group was significantly decreased (P < 0.05), SDNN of the intervention group was significantly increased (P < 0.05) and LF/HF was significantly decreased (P < 0.05). Conclusion: The resistance training intervention adopted in this study significantly increased the HRV of anxious female college students and improved their autonomic nervous disorder.

Phase information extraction for moiré fringes based on multiresolution analysis.

In this paper, the multiresolution analysis (MRA) method is used to preprocess moiré fringes, which can reduce the number of data points and increase computation speeds. To discuss the applicability of the method, a candle combustion flow field is chosen as an example for experiment by moiré deflectometry. First, moiré fringes are preprocessed by the MRA method. Then, phase information extraction and refractive index reconstruction are performed on the three-level low-frequency approximation components. Finally, the involved results prove that the calculation time required for phase information extraction and refractive index reconstruction is greatly reduced based on the moiré fringes preprocessed by MRA method. The relative error could be accepted if the suitable approximation level is applied.