Object extraction is an important tool in many applications within the image processing and computer vision communities. You Only Look Once version 3 (YOLOv3) has been extensively applied to many fields as a state-of-the-art technique for object semantic detection. Despite its numerous characteristics, YOLOv3 has to be combined with appropriate image segmentation technologies to achieve effective 2D object extraction in real-time monitoring, robot navigation, and target search. In this paper, the K-means algorithm is applied to the segmentation of depth images. Considering the inherent sensitivity to the randomness of the initial cluster center and the uncertainty of cluster number K in the initialization phase of the K-means algorithm, this paper proposes a new method that combines the semantic image information with the image depth information. Specifically, this method proposed to pre-classify the center depth of the object to determine the appropriate value of K required in the K-means algorithm. At the same time, the proposed algorithm improves the selection of the initial center via the maximin method. This paper introduces a multi-parameter extraction method to enable to correctly identify the object of interest after image segmentation. The technique considers three parameters to achieve this: i) the elements of size, ii) the connected domain, and iii) the diagonal detection. Experiments using open-source datasets demonstrate that the average processing time and the segmentation accuracy of the improved K-means algorithm are 20.36% faster and 3.12% higher than the conventional K-means algorithm, respectively. The extraction accuracy of the proposed method is 6.69% higher than that of the SuperCut extraction method.

Pipeline leakage or explosion has caused huge economic losses, polluted the environments and threatened the safety of civilian's lives and assets, which even caused negative influences to the society greatly. Fortunately, pipeline inspection gauge (PIG) could accomplish the pipeline defect (corrosions, cracks, grooves, etc.) inspection effectively and meanwhile to localize these defects precisely by navigation sensors. The results are utilized for pipeline integrity management (PIM) and pipeline geographic information system construction. Generally, the urban underground pipeline presents with smalldiameter and complicated-distribution properties, which are of great challenges for the pipeline defects positioning by PIG. This chapter focuses on in-depth research of the highprecision positioning method for small-diameter PIG navigation. In the beginning, the problems and system errors statement of MEMS SINS-based PIG are analyzed step by step. Then, the pipeline junction (PJ) identification method based on fast orthogonal search (FOS) is studied. After that, a PIG positioning system that comprises of microinertial/AGM/odometer/PJ is proposed, and also the application mechanism of extended Kalman filter and its smoothing technology on PIG navigation system is researched to improve the overall positioning precision for the small-diameter PIG. Finally, the proposed methods and research conclusions are verified by the indoor wheel robot simulation platform.

To solve the self-alignment problem of the Strapdown Inertial Navigation System (SINS), a novel adaptive filter based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) is proposed. The Gravitational Apparent Motion (GAM) is used in the coarse alignment, and the problem of obtaining the attitude matrix between the body frame and the navigation frame is attributed to obtaining the matrix between the initial body frame and the current navigation frame using two gravitational apparent motion vectors at different moments. However, the accuracy and time of this alignment method always suffer from the measurement noise of sensors. Thus, a novel adaptive filter based on CEEMD using an l 2 -norm to calculate the similarity measure between the Probability Density Function (PDF) of each Intrinsic Mode Function (IMF) and the original signal is proposed to denoise the measurements of the accelerometer. Furthermore, the advantage of this filter is verified by comparing with other conventional denoising methods, such as PDF-based EMD (EMD-PDF) and the Finite Impulse Response (FIR) digital low-pass filter method. The results of the simulation and experiments indicate that the proposed method performs better than the conventional methods in both alignment time and alignment accuracy.

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Vehicles have to rely on satellite navigation in an open environment. However, satellite navigation cannot obtain accurate positioning information for vehicles in the interior of underground parking lots, as they comprise a semi-enclosed navigation space. Therefore, vehicular navigation needs to take into consideration both outdoor and indoor environments. Actually, outdoor navigation and indoor navigation require different positioning methods, and it is of great importance to choose a reasonable navigation and positioning algorithm solution for vehicles. Fortunately, the integrated navigation of the Global Positioning System (GPS) and the Micro-Electro-Mechanical System (MEMS) inertial navigation system could solve the problem of switching navigation algorithms in the entrance and exit of underground parking lots. This paper proposes a low cost vehicular seamless navigation technology based on the reduced inertial sensor system (RISS)/GPS between the outdoors and an underground garage. Specifically, the enhanced RISS is a positioning algorithm based on three inertial sensors and one odometer, which could achieve a similar location effect as the full model integrated navigation, reduce the costs greatly, and improve the efficiency of each sensor.