Kullback-leibler divergence based graph pruning in robotic feature mapping
Kullback-leibler divergence based graph pruning in robotic feature mapping
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DOI：
10.1109/ecmr.2013.6698816
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Yue Wang;R. Xiong;Qianshan Li;Shoudong Huang
中科院分区：
文献类型：
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作者：
Yue Wang;R. Xiong;Qianshan Li;Shoudong Huang
在位姿特征图同步定位与映射中，将机器人的位姿和特征位置作为图节点，将测程和观测值作为边。图的大小对图优化的效率有重要的影响。通常，如果当前帧在空间上与前一帧的距离不够远，或者信息量不够，则通过丢弃当前帧来保持图的大小较小。然而，当机器人再次访问先前探索过的区域时，这些方法不能丢弃已经保存的帧。我们提出了一种基于Kullbach-Leibler散度的度量来决定一帧是否应该被丢弃，实现了特征映射的图剪枝算法的在线实现，剪枝后的帧可以是任何保留的帧。使用真实数据集的实验结果表明，所提出的剪枝算法可以有效地减小图的大小，同时保持图的精度。
In pose feature graph simultaneous localization and mapping, the robot poses and feature positions are treated as graph nodes and the odometry and observations are treated as edges. The size of the graph exerts an important influence on the efficiency of the graph optimization. Conventionally, the size of the graph is kept small by discarding the current frame if it is not spatially far enough from the previous one or not informative enough. However, these approaches cannot discard the already preserved frames when the robot re-visits the previously explored area. We propose a measure derived from Kullbach-Leibler divergence to decide whether a frame should be discarded, achieving an online implementation of the graph pruning algorithm for feature mapping, of which the pruned frame can be any of the preserved frames. The experimental results using real world datasets show that the proposed pruning algorithm can effectively reduce the size of the graph while maintaining the map accuracy.