SuMa++: Efficient LiDAR-based Semantic SLAM
This work addresses reliable localization and mapping for autonomous systems in dynamic environments, representing an incremental improvement over existing surfel-based methods.
The paper tackles the problem of dynamic objects corrupting LiDAR-based SLAM by integrating semantic segmentation to filter moving objects and improve scan matching, achieving improved performance on challenging KITTI highway sequences compared to a geometric state-of-the-art approach.
Reliable and accurate localization and mapping are key components of most autonomous systems. Besides geometric information about the mapped environment, the semantics plays an important role to enable intelligent navigation behaviors. In most realistic environments, this task is particularly complicated due to dynamics caused by moving objects, which can corrupt the mapping step or derail localization. In this paper, we propose an extension of a recently published surfel-based mapping approach exploiting three-dimensional laser range scans by integrating semantic information to facilitate the mapping process. The semantic information is efficiently extracted by a fully convolutional neural network and rendered on a spherical projection of the laser range data. This computed semantic segmentation results in point-wise labels for the whole scan, allowing us to build a semantically-enriched map with labeled surfels. This semantic map enables us to reliably filter moving objects, but also improve the projective scan matching via semantic constraints. Our experimental evaluation on challenging highways sequences from KITTI dataset with very few static structures and a large amount of moving cars shows the advantage of our semantic SLAM approach in comparison to a purely geometric, state-of-the-art approach.