When the image is blurred due to rapid camera movement or in low-texture scenes， the Simultaneous Localization And Mapping （SLAM） algorithm using only point features is difficult to track and extract enough feature points， resulting in poor positioning accuracy and matching robustness. If it causes false matching， even the system cannot work. To solve the problem， a low-texture monocular SLAM algorithm based on point-line feature fusion was proposed. Firstly， the line features were added to enhance the system stability， and the problem of insufficient extraction of point feature algorithm in low texture scenes was solved. Then， the idea of weighting was introduced for the extraction number selection of point and line features， and the weight of point and line features were allocated reasonably according to the richness of the scene. The proposed algorithm ran in low-texture scenes， so the line features were set as the main features and the point features were set as the auxiliary features. Experimental results on the TUM indoor dataset show that compared with the existing point-line feature algorithms， the proposed algorithm can effectively improve the matching precision of the line features， has the trajectory error reduced by about 9 percentage points， and has the feature extraction time reduced by 30 percentage points. As the result， the added line features play a positive and effective role in low-texture scenes， and improve the overall accuracy and reliability of the data.

As weak bioelectricity signals, diaphragmatic electromyographic (EMGdi) signals are always corrupted by strong electrocardiography (ECG) signals. A denoising algorithm based on wavelet scalogram adaptive threshold was proposed in this paper to improve the precision of threshold in EMGdi signal denoising. This algorithm found the position of ECG interference by performing wavelet transform on the EMGdi signals and conveying wavelet coefficients to wavelet scalogram, and then automatically adjusted the threshold by ECG neighborhood wavelet energy in order to remove ECG interference. By comparing the results with the wavelet threshold, it shows that the proposed method can eliminate the ECG interference from EMGdi and reserve EMGdi signal characteristics effectively.

In order to compute approximate geodesic efficiently between two points on point cloud, a weighted graph was constructed by splitting point cloud along the Cartesian coordinate axes, thus initial approximate geodesic between any two given points could be computed out using Dijkstra's algorithm. Then the conjugate gradient method was adopted to minimize the energy function defined; finally, approximate geodesic could be obtained after some iterative steps. This proposed algorithm avoids meshing or reconstructing the point cloud to be local or global surface, and it is suitable for computing geodesic on large scale point cloud.