Robust Active Perception via Data-association aware Belief Space planning
Robust Active Perception via Data-association aware Belief Space planning
复制标题
通过数据关联感知信念空间规划实现稳健的主动感知
DOI：
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发表时间：
2016
期刊：
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通讯作者：
V. Indelman
中科院分区：
文献类型：
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作者：
Shashank Pathak;Antony Thomas;Asaf Feniger;V. Indelman
我们开发了一种信念空间规划（BSP）方法，通过在规划中纳入有关数据关联（DA）的推理，同时考虑到其他不确定性来源，从而提高了技术水平。现有的BSP方法通常假设数据关联是给定的和完美的，在存在定位不确定性的情况下，在模糊和感知混淆的环境中，这种假设在操作时可能更难证明。相比之下，我们的数据关联感知信念空间规划（DA- bsp）方法明确地解释了信念进化中的DA，因此可以更好地适应这些具有挑战性的现实世界场景。特别是，我们表明，由于感知混叠，后验信念成为概率分布函数和设计成本函数的混合物，这些函数测量了期望的模糊性和后验不确定性水平。在目标函数中使用这些和标准成本（例如，控制惩罚，到目标的距离），可以产生一个可靠地表示行动影响的一般框架，特别是能够主动消除歧义。因此，我们的方法适用于感知混淆环境中的鲁棒主动感知和自主导航。我们在基本和现实的模拟中演示了关键方面。
We develop a belief space planning (BSP) approach that advances the state of the art by incorporating reasoning about data association (DA) within planning, while considering additional sources of uncertainty. Existing BSP approaches typically assume data association is given and perfect, an assumption that can be harder to justify while operating, in the presence of localization uncertainty, in ambiguous and perceptually aliased environments. In contrast, our data association aware belief space planning (DA-BSP) approach explicitly reasons about DA within belief evolution, and as such can better accommodate these challenging real world scenarios. In particular, we show that due to perceptual aliasing, the posterior belief becomes a mixture of probability distribution functions, and design cost functions that measure the expected level of ambiguity and posterior uncertainty. Using these and standard costs (e.g.~control penalty, distance to goal) within the objective function, yields a general framework that reliably represents action impact, and in particular, capable of active disambiguation. Our approach is thus applicable to robust active perception and autonomous navigation in perceptually aliased environments. We demonstrate key aspects in basic and realistic simulations.