Recurrent-OctoMap: Learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data

This letter presents a novel semantic mapping approach, Recurrent-OctoMap, learned from long-term three-dimensional (3-D) Lidar data. Most existing semantic mapping approaches focus on improving semantic understanding of single frames, rather than 3-D refinement of semantic maps (i.e. fusing semantic observations). The most widely used approach for the 3-D semantic map refinement is ``Bayes 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 3-D map as an OctoMap, and model each cell as a recurrent neural network, to obtain a Recurrent-OctoMap. In this case, the semantic mapping process can he 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 3-D localization and mapping system for successively mapping a dynamic environment using more than two weeks of data, and the system can he trained and deployed with arbitrary memory length. We validate our approach on the ETH long-term 3-D Lidar dataset. The experimental results show that our proposed approach outperforms the conventional ``Bayes update'' approach.