论文标题
特色轨迹生成轨迹嘴
Featured Trajectory Generation for TrackPuzzle
论文作者
论文摘要
室内路线图学习对于自动室内导航至关重要。众包室内路线图学习的关键问题是轨迹生成的特色。在本文中，提供了一个系统来通过众包智能手机数据来生成特色轨迹。首先，我们提出了一种更准确的PDR算法，以生成轨迹运动数据。该算法使用自适应作为步骤计数方法，并使用步骤估计算法o使轨迹的长度更加准确。接下来，晴雨表用于分割不同楼层的轨道，轨道地板是通过WiFi特征群集获得的。最后，通过将转弯点作为轨迹的特征点，提取轨迹的顶点和边缘以减少长直轨迹的噪声。
Indoor route graph learning is critically important for autonomous indoor navigation. A key problem for crowd-sourcing indoor route graph learning is featured trajectory generation. In this paper, a system is provided to generate featured trajectories by crowd-sourcing smartphone data. Firstly, we propose a more accurate PDR algorithm for the generation of trajectory motion data. This algorithm uses ADAPTIV as the step counting method and uses the step estimation algorithm o make the trajectory more accurate in length. Next, the barometer is used to segment the tracks of different floors, and the track floors are obtained by WiFi feature clustering. Finally, by finding the turning point as the feature point of the trajectory, the vertices and edges of the trajectory are extracted to reduce the noise of the long straight trajectory.
