Practical Persistence Reasoning in Visual SLAM
Practical Persistence Reasoning in Visual SLAM
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DOI：
10.1109/icra40945.2020.9196913
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发表时间：
2020-05
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影响因子：
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通讯作者：
Z. S. Hashemifar;Karthik Dantu
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作者：
Z. S. Hashemifar;Karthik Dantu
许多现有的SLAM方法依赖于静态环境的假设以获得准确的性能。然而，一些机器人应用要求它们在半静态或动态环境中反复遍历。最近有一些研究兴趣设计持久性过滤器，以推理这种情况下的持久性。我们在这项工作中的目标是将这种持久性推理在视觉SLAM。为此，我们将持久性过滤器[1]纳入ORB-SLAM中，ORB-SLAM是一种着名的视觉SLAM算法。我们观察到，他们的建议的简单集成导致效率低下的持久性推理。通过一系列的修改，并使用两个本地收集的数据集，我们证明了这种持久性过滤的效用，以及我们的定制ORB-SLAM。总的来说，结合持久性过滤可以显著减少map大小（最好的情况下约为30%），并相应减少运行时间，同时保持与使用更大map的方法相似的准确性。
Many existing SLAM approaches rely on the assumption of static environments for accurate performance. However, several robot applications require them to traverse repeatedly in semi-static or dynamic environments. There has been some recent research interest in designing persistence filters to reason about persistence in such scenarios. Our goal in this work is to incorporate such persistence reasoning in visual SLAM. To this end, we incorporate persistence filters [1] into ORB-SLAM, a well-known visual SLAM algorithm. We observe that the simple integration of their proposal results in inefficient persistence reasoning. Through a series of modifications and using two locally collected datasets, we demonstrate the utility of such persistence filtering as well as our customizations in ORB-SLAM. Overall, incorporating persistence filtering could result in a significant reduction in map size (about 30% in the best case) and a corresponding reduction in run-time while retaining similar accuracy to methods that use much larger maps.