Terrain Classification is an essential task in space exploration, where unpredictable environments are difficult to observe using only exteroceptive sensors such as vision. Implementing Neural Network classifiers can have high performance but can be deemed untrustworthy as they lack transparency, which makes them unreliable for taking high-stakes decisions during mission planning. We address this by proposing Neural Networks with Uncertainty Quantification in Terrain Classification. We enable our Neural Networks with Monte Carlo Dropout, DropConnect, and Flipout in time series-capable architectures using only proprioceptive data as input. We use Bayesian Optimization with Hyperband for efficient hyperparameter optimization to find optimal models for trustworthy terrain classification.

翻译：地形分类是太空探索中的关键任务，在不可预测的环境中仅依靠视觉等外部感知传感器难以进行有效观测。神经网络分类器虽能实现高性能，但由于缺乏透明度而可能被视为不可信，这使得其在任务规划中承担高风险决策时不可靠。为此，我们提出在神经网络地形分类中引入不确定性量化方法。我们通过在适用于时间序列的架构中集成蒙特卡洛Dropout、DropConnect和Flipout技术，仅使用本体感知数据作为输入来实现神经网络的不确定性估计。采用结合Hyperband的贝叶斯优化方法进行高效超参数优化，以寻找可信地形分类的最优模型。