For Lifelong SLAM, one has to deal with temporary localization failures, e.g., induced by kidnapping. We achieve this by starting a new map and merging it with the previous map as soon as relocalization succeeds. Since relocalization methods are fallible, it can happen that such a merge is invalid, e.g., due to perceptual aliasing. To address this issue, we propose methods to detect and undo invalid merges. These methods compare incoming scans with scans that were previously merged into the current map and consider how well they agree with each other. Evaluation of our methods takes place using a dataset that consists of multiple flat and office environments, as well as the public MIT Stata Center dataset. We show that methods based on a change detection algorithm and on comparison of gridmaps perform well in both environments and can be run in real-time with a reasonable computational cost.
翻译:对于终身的SLAM, 需要处理暂时的本地化故障, 例如绑架引起的。 我们通过在重新本地化成功后启动新地图并将其与上一张地图合并来实现这一点。 由于重新本地化方法可以推倒, 这样的合并可能发生无效, 例如由于认知化别名。 为了解决这个问题, 我们建议了检测和撤销无效合并的方法。 这些方法可以将收到的扫描与先前并入当前地图的扫描进行比较, 并考虑它们之间如何相互一致。 评估我们的方法时使用一个由多个公寓和办公环境组成的数据集, 以及MIT Stata中心的公共数据集。 我们表明,基于变化检测算法和电网图比较的方法在两种环境中都运作良好, 并且可以实时运行, 并使用合理的计算成本 。