Li Sun, Zhi Yan, Anestis Zaganidis, Cheng Zhao, and Tom Duckett
Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data
IEEE Robotics and Automation Letters (Volume: 3, Issue: 4, 2018)
Abstract
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 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 3-D 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 3-D Lidar dataset. The experimental results show that our proposed approach outperforms the conventional “Bayes update” approach.
@article{Sun2018,
doi = {10.1109/lra.2018.2856268},
url = {https://doi.org/10.1109/lra.2018.2856268},
year = {2018},
month = {oct},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
volume = {3},
number = {4},
pages = {3749--3756},
author = {Li Sun and Zhi Yan and Anestis Zaganidis and Cheng Zhao and Tom Duckett},
title = {Recurrent-{OctoMap}: Learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data},
journal = {{IEEE} Robotics and Automation Letters}
}