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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]

In this paper, global positioning system (GPS) data collected by seven permanent GPS stations of Southern California Integrated GPS Network during the launch of the long-range Minuteman II missile from Vandenberg Air Force Base on July 8, 2000 is processed. By manipulating and passing the total electron content through a high-pass filter, the energy released by the missile launch was detected. After that, the basic concept of positioning used in GPS is applied to the problem to pinpoint the position of the missile launch pad. This is done by the implementation of the concept of subionospheric points that serve analogously to GPS satellites. The simulation of the analysis with prior data proves that the launch pad can indeed be positioned with this algorithm. However, real-life analysis without prior data highlights the existence of three problems that need to be overcome to pinpoint that pad. [ABSTRACT FROM AUTHOR]