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A Modular Optimization Framework for Localization and Mapping

This work approaches the challenge of how to divide the problem of Simultaneous Localization and Mapping (SLAM) into its smallest possible constituents, in such a way that the reusability and interchangeability of each such module is maximized. In particular, most components in the proposed system should be not aware of details such that whether the map comprises a single global map or a set of local submaps, whether the state vector is defined in SE(2) or SE(3), with or without velocity, etc. Any number of heterogeneous sensors should be used together and their information fused seamlessly into a consistent localization solution. The resulting system would be useful for researchers, easing the development of reproducible research and enabling the quick adoption of state-of-the-art algorithms into product prototypes. Our implementation has been tested with different sensors against the KITTI, EuRoC, and KAIST datasets. In this paper we focus on an introduction to the framework and on experimental results for 3D LiDAR odometry and mapping. LiDAR SLAM for the KITTI datasets achieves typical translation errors of 1%–2% for most urban sequences, while processing the data at 1.5x the real-time rate with a reduced memory requirement thanks to our framework’s capability to dynamically swap out from memory the parts of the map that are not immediately required, transparently loading them again when required. The framework will be released as open-source at https://github.com/MOLAorg/mola