The existing machine-vision surface roughness measurement technique extracts relevant evaluation indices from grayscale images without using the strong sensitivity of color information. In addition, most of these measurements use a micro-vision imaging method to measure a small area and cannot make an overall assessment of the workpiece's surface. To address these issues, a method of measuring surface roughness that uses an ordinary light source and a macro-vision perspective to generate a red and green color index for each pixel is proposed in the present study. A comparison test is conducted on a set of test samples before and after surface contamination using the color index and gray-level algebraic averaging, the square of the main component of the Fourier transform in the frequency domain, and the entropy. A strong correlation between the color index and the surface roughness is established; this correlation is not only higher than that of other indices but also present despite contamination and very robust. Verification using a regression model based on a support vector machine proves that the proposed method not only has a simple apparatus and makes measurement easy but also provides high precision and is suitable over a wide measurement range. The impact of the red and green color blocks, the lighting, and the direction of the surface texture on the correlation between the color index and the roughness are also assessed and discussed in this paper.

A positioning system in the absence of GPS is important in establishing indoor directional guidance and localization. Inertial Measuring Units (IMUs) can be used to detect the movement of a pedestrian. In this paper, we present a three-dimensional (3D) indoor positioning system using foot mounted low cost Micro-Electro-Mechanical System (MEMS) sensors to locate the position and attitude of a person in 3D view, and to plot the path travelled by the person. The sensors include accelerometers, gyroscopes, and a barometer. The pedestrians motion information is collected by accelerometers and gyroscopes to achieve Pedestrian Dead-Reckoning (PDR) which is used to estimate the pedestrian's rough position. A zero velocity update (ZUPT) algorithm is developed to detect the standing still moment. A Kalman filter is combined with the ZUPT to eliminate non-linear errors in order to obtain accurate positioning information of a pedestrian. The information collected by the barometer is integrated with the accelerometer data to detect the altitude changes and to obtain accurate height information. The main contribution of this research is that the approach proposed fuses barometer and accelerometer in Kalman filter to obtain accurate height information, which has improved the accuracy at x axis and y axis. The proposed system has been tested in several simulated scenarios and real environments. The distance errors are around 1%, and the positioning errors are less than 1% of the total travelled distance. Results indicate that the proposed system performs better tha n other similar systems using the same low-cost IMUs.

For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m.

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