Indoor Localization Based on VIO System and Three-Dimensional Map Matching.

High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments.

Improving Positioning Accuracy via Map Matching Algorithm for Visual-Inertial Odometer.

A visual-inertial odometer is used to fuse the image information obtained by a vision sensor with the data measured by an inertial sensor and recover the motion track online in a global frame. However, in an indoor environment, geometric transformation, sparse features, illumination changes, blurring, and noise will occur, which will either cause a reduction in or failure of the positioning accuracy. To solve this problem, a map matching algorithm based on an indoor plane structure map is proposed to improve the positioning accuracy of the system; this algorithm was implemented using a conditional random field model.

Research on PF-SLAM Indoor Pedestrian Localization Algorithm Based on Feature Point Map.

Recently, the map matching-assisted positioning method based on micro-electromechanical systems (MEMS) inertial devices has become a research hotspot for indoor pedestrian positioning; however, these are based on existing indoor electronic maps. In this paper, without prior knowledge of the map and through building an indoor main path feature point map combined with the simultaneous localization and map building (SLAM) particle filter (PF-SLAM) algorithm idea, a PF-SLAM indoor pedestrian location algorithm based on a feature point map was proposed through the inertial measurement unit to improve indoor pedestrian positioning accuracy. Aiming at the problem of inaccurate heading angle estimation in the pedestrian dead reckoning (PDR) algorithm, a turn-straight-state threshold detection method was proposed that corrected the difference of the heading angles during the straight-line walking of pedestrians to suppress the error accumulation of the heading angle.

Indoor Pedestrian Navigation Based on Conditional Random Field Algorithm.

Foot-mounted micro-electromechanical systems (MEMS) inertial sensors based on pedestrian navigation can be used for indoor localization. We previously developed a novel zero-velocity detection algorithm based on the variation in speed over a gait cycle, which can be used to correct positional errors. However, the accumulation of heading errors cannot be corrected and thus, the system suffers from considerable drift over time.

A Novel Pedestrian Navigation Algorithm for a Foot-Mounted Inertial-Sensor-Based System.

This paper proposes a novel zero velocity update (ZUPT) method for a foot-mounted pedestrian navigation system (PNS). First, the error model of the PNS is developed and a Kalman filter is built based on the error model. Second, a novel zero velocity detection algorithm based on the variations in speed over a gait cycle is proposed.

[Fetal electrocardiogram extraction based on nonlinear time series analysis].

This paper reports the applications of a local non-linear projection technique to extract the fetal component in single-lead recordings from the abdomen of a pregnant woman. We propose the general planning, and then we determine and illustrate four important parameters in the algorithm. The results of experiments show that the method is effective for separating the fetal ECG from the maternal trace and from the other contaminations.

[A new method of extracting FECG by BSS of sparse signal].

The electrocardiograph signal presents the sparseness. In this paper, a new method of extracting the Fetal Electrocardiograph (FECG) is proposed, which uses the Blind Source Separation(BSS) of Sparse Signal. Both the Wavelet Analysis method and a new scheme named Vanish Circle are also involved in order to avoid the influence on the signal separation, because the ECG signals are not completed sparse signals.