Monte Carlo Dropoutを適用したEnd-to-End自己位置推定とMCLの融合

抄録

This paper presents a hybrid localization method using model- and learning-based methods. Monte Carlo localization (MCL) is used as a model-based method. End-to-end (E2E) learning is used to implement a learning-based localization method. Monte Carlo dropout is applied to the E2E localization and its output is treated as a probabilistic distribution. This distribution is then used as a proposal distribution and the E2E localization estimate is fused with MCL via importance sampling. Experimental results show that both the advantages are simultaneously leveraged while mitigating their disadvantages.