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. To mitigate this issue 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. Video - https://youtu.be/5xVGm7z9c-0

翻译：将语义信息集成到地图中，能使机器人更好地理解环境并做出高层级决策。近年来，神经网络在感知能力方面取得了巨大进展。然而，当将神经网络的多次观测结果融合至语义地图时，其固有对未知数据的过度自信会导致异常值权重过大，从而降低鲁棒性。为解决这一问题，我们提出一种新的鲁棒融合方法，用于组合多个贝叶斯语义预测。该方法利用贝叶斯神经网络提供的不确定性估计来校准观测值的融合方式。具体通过正则化观测值以缓解对异常预测过度自信的问题，并利用认知不确定性权衡其在融合中的影响力，从而形成概率分布的差异化表达。我们通过在逼真模拟环境和真实场景中进行实验验证了该鲁棒融合策略。两种情况下均使用在不同数据上训练的网络，使模型暴露于不同数据分布中。结果表明，相较于其他方法，考虑模型不确定性并对观测概率分布进行正则化处理，能实现更优的语义分割性能，并对异常值具有更强的鲁棒性。视频链接：https://youtu.be/5xVGm7z9c-0