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This paper presents a novel approach for vision-based Simultaneous Localization and Mapping (SLAM) on a high curved track without pose-based landmarks. The proposed approach combines an Iterative Closest Points (ICP) method and Stochastic Gradient Descent (SGD) optimization and comprises four main steps. First, a Kalman filter with a simple circular lane model is used to estimate the road curvature using images from a front camera. Then, the vehicle position and orientation are reconstructed by using the yaw rate and longitudinal speed from inertial sensors. Drift and misalignment of the constructed map are corrected using ICP under the assumption that the vehicle continuously travels the same track. The final map is obtained using SGD optimization, which enforces curvature matching. We evaluate the performance of the proposed algorithm with an environment of the winding track of Hyundai-Kia California Proving Ground (CPG) located in Southern California and the customized ellipsoidal track. The experimental results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the spring-block model proposed by Olami, Feder, and Christensen (OFC), we obtained a time series of synthetic earthquakes with different values of the conservation level ( β ), which measures the fraction of the energy that a relaxing block passes to its neighbors. The time series have multifractal characteristics, and we analyzed them with the Chhabra and Jensen method. We calculated the width, symmetry, and curvature parameters for each spectrum. As the value of conservation level increases, the spectra widen, the symmetric parameter increases, and the curvature around the maximum of the spectra decreases. In a long series of synthetic seismicity, we located earthquakes of the greatest magnitude and built overlapping windows before and after them. For the time series in each window, we performed multifractal analysis to obtain multifractal spectra. We also calculated the width, symmetry, and curvature around the maximum of the multifractal spectrum. We followed the evolution of these parameters before and after large earthquakes. We found that the multifractal spectra had greater widths, were less skewed to the left, and were very pointed around the maximum before rather than after large earthquakes. We studied and calculated the same parameters and found the same results in the analysis of the Southern California seismicity catalog. This suggests that there seems to be a process of preparation for a great earthquake and that its dynamics are different from the one that occurs after this mainshock based on the behavior of the parameters mentioned before. [ABSTRACT FROM AUTHOR]