  This paper presents a novel semantic mapping approach, Recurrent-OctoMap,
learned from long-term 3D Lidar data. Most existing semantic mapping approaches
focus on improving semantic understanding of single frames, rather than 3D
refinement of semantic maps (i.e. fusing semantic observations). The most
widely-used approach for 3D semantic map refinement is a Bayesian update, which
fuses the consecutive predictive probabilities following a Markov-Chain model.
Instead, we propose a learning approach to fuse the semantic features, rather
than simply fusing predictions from a classifier. In our approach, we represent
and maintain our 3D map as an OctoMap, and model each cell as a recurrent
neural network (RNN), to obtain a Recurrent-OctoMap. In this case, the semantic
mapping process can be formulated as a sequence-to-sequence encoding-decoding
problem. Moreover, in order to extend the duration of observations in our
Recurrent-OctoMap, we developed a robust 3D localization and mapping system for
successively mapping a dynamic environment using more than two weeks of data,
and the system can be trained and deployed with arbitrary memory length. We
validate our approach on the ETH long-term 3D Lidar dataset [1]. The
experimental results show that our proposed approach outperforms the
conventional "Bayesian update" approach.
