It is found that the vast majority of the information matrix elem

It is found that the vast majority of the information matrix elements are close to zero, while the information matrix is dominated by a small number sellekchem of diagonal elements [20]. If the elements are made approximately close to zero, that is pruning weak links in the information Inhibitors,Modulators,Libraries matrix, an approximate representation which is the so-called Sparse Extended Information Filter (SEIF) proposed by Thrun et al. is obtained [21,22]. All the basic update formulae can be implemented in constant time, irrespective of the size of the map, which greatly improves computational complexity. There are a variety of new information filter-based algorithms such as TJFT [23], Treemap [24], ESEIF [25,26], but theoretical analysis and experimental evidence show that SEIF-SLAM is computationally efficient and consistent in the relative map, so the SEIF-SLAM algorithm is preferred in our work.
The C-Ranger AUV was developed in Underwater Vehicle Laboratory in Ocean University of China. It is equipped with a variety of onboard equipment for sensing vehicle pose and environment. In this Inhibitors,Modulators,Libraries paper we mainly address the autonomous navigation method for the C-Ranger AUV. To verify the advantages of SEIF-SLAM, the application of SEIF-SLAM in AUV navigation via sea trial experiments has been studied; also the data processing approach of sonar had been presented in this work. For the mechanical scanning imaging sonar chosen as the principal sensor for active sensing of undersea obstacles, a compensation method based on feedback of the AUV pose had been used to overcome distortion of the acoustic images due to the vehicle motion.
Inhibitors,Modulators,Libraries Sea trials in Tuandao Bay were carried out to verify the feasibility of the proposed methods. The experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improved the accuracy of navigation when compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM.The remainder of the paper is organized as follows: in the next section, the SEIF-SLAM algorithm for our C-Ranger AUV is presented, including the main processing steps. Section 3 describes the C-Ranger AUV and on-board sensors employed in the SLAM module. Further, sonar data processing is discussed in Section 4, including point-feature extraction and motion-distortion compensation.
The sea trial experiments is described in Section Inhibitors,Modulators,Libraries 5, and the performance of the proposed navigation method is evaluated; then the results and possible future improvements are discussed in Section 6. Finally, we summarize the results and draw the fundamental conclusions.2.?A AV-951 SEIF-SLAM Algorithm for the C-Ranger AUVAUV travels undersea at a certain depth in most cases, GW 572016 so the bidimensional vehicle-landmark model was adopted to represent the AUV and landmarks (also called features) in the undersea environment.

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