The quality of navigation service is determined by the accuracy of the available data. For existing navigation services, a full map update is provided in order to keep the map data of mobile devices current. As content and services of mobile devices have recently been diversifying, the size of map data managed in mobile devices has increased, reaching several gigabytes in size. It generally takes tens of minutes to write several gigabytes of data into mobile device storage. For traditional navigation systems, a complicated storage structure called a physical storage format (PSF) is used to assure maximum processing performance of map data in mobile devices within limited resources. Consequently, even though modified navigation map data actually affects only a portion of a map, the full map data is updated because partial updates are not possible. In this paper, a navigation system is studied to solve this difficult partial map update problem. The map air update navigation system, which is the result of this study, provides real-time partial map updating using wireless communications.
Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)‐based direct odometry, which uses a spherical range image (SRI) that projects a three‐dimensional point cloud onto a two‐dimensional spherical image plane. Direct odometry is developed in a vision‐based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state‐of‐the‐art and remarkably higher speed than conventional techniques.
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