Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.
Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.
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DOI：
10.1109/percomworkshops51409.2021.9431082
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Manduchi R
中科院分区：
文献类型：
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作者：
Tsai CH;Ren P;Elyasi F;Manduchi R
为了辅助返回，我们对基于惯性的里程计算法进行了比较分析。辅助返回系统便于对先前采取的路径进行回溯，并且对盲人行人特别有用。我们提出了一种新的路径匹配算法，并在模拟的辅助返回任务中用WeAllWalk的数据进行了测试，WeAllWalk是现存的唯一具有盲人记录的惯性数据的数据集。我们考虑了两种里程计系统，一个基于深度学习(RONIN)，另一个基于稳健的转弯检测和步数计算。结果表明，使用转数/步数里程计系统可以获得最佳的路径匹配结果。
We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system.