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能让机器人更好地理解环境并做出高层决策。近年来，神经网络在感知能力方面取得了巨大进步。然而，当将神经网络的多次观测融合到语义地图时，其对未知数据固有的过度自信会赋予异常值过大权重，从而降低最终地图的鲁棒性。本研究提出一种新型鲁棒融合方法，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数据融合方式，具体通过正则化观测值缓解过度自信的异常预测问题，并利用认知不确定性权衡其在融合中的影响，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开展实验验证所提鲁棒融合策略。两种场景下均使用在不同数据上训练的模型，以暴露模型面对不同数据分布时的表现。结果表明，与其他方法相比，考虑模型不确定性并对观测概率分布进行正则化处理，能提升语义分割性能并增强对异常值的鲁棒性。