The integration of semantic information in a map allows robots to understand better their environment and make high-level decisions. In the last few years, neural networks have shown enormous progress in their perception capabilities. However, when fusing multiple observations from a neural network in a semantic map, its inherent overconfidence with unknown data gives too much weight to the outliers and decreases the robustness of the resulting map. In this work, we propose a novel robust fusion method to combine multiple Bayesian semantic predictions. Our method uses the uncertainty estimation provided by a Bayesian neural network to calibrate the way in which the measurements are fused. This is done by regularizing the observations to mitigate the problem of overconfident outlier predictions and using the epistemic uncertainty to weigh their influence in the fusion, resulting in a different formulation of the probability distributions. We validate our robust fusion strategy by performing experiments on photo-realistic simulated environments and real scenes. In both cases, we use a network trained on different data to expose the model to varying data distributions. The results show that considering the model's uncertainty and regularizing the probability distribution of the observations distribution results in a better semantic segmentation performance and more robustness to outliers, compared with other methods.
翻译:将语义信息整合到地图中, 使机器人能够更好地了解环境, 并做出高层次的决定。 过去几年里, 神经网络在其感知能力方面表现出了巨大的进步。 但是, 当在语义地图中将神经网络的多次观测结果冻结在语义图中时, 其内在的过度自信与未知数据给外端带来太大的分量, 从而降低了由此绘制的地图的稳健性。 在这项工作中, 我们提出了一种新型的强力聚合法, 将多种巴耶西亚语义预测结合起来。 我们的方法使用巴耶斯神经网络提供的不确定性估计来校准测量结果的结合方式。 这样做的方法是定期进行观察, 以减轻过分信任性外向预测的问题, 并使用认知性不确定性来权衡其在聚合中的影响, 导致不同的概率分布方式。 我们通过对摄影现实的模拟环境和真实场景进行实验来验证我们的强性融合战略。 在这两种情况下, 我们使用一个经过训练的不同数据网络来将模型暴露为不同的数据分布方式。 其结果显示, 将模型的准确性与正常性进行更精确的分布, 以更精确的方式将模型进行对比。</s>