Data Association Aware Semantic Mapping and Localization via a ViewpointDependent Classifier Model We present an approach for localization and semantic mapping in ambiguous scenarios by incrementally maintaining a hybrid belief over continuous states and discrete classification and data association variables. Unlike existing incremental approaches, we explicitly maintain data association components over time, which allows us to deal with perceptual aliasing. Crucially, we utilize a viewpointdependent classifier model over rich classifier outputs and leverage the coupling between poses and semantic measurements both for disambiguating data association and in pose estimation. We demonstrate in simulation that incorporating semantic measurements with a viewpointdependent classifier model enhances disambiguation of both data association and localization over usage of only geometric measurements or viewpoint independent models, further contributing to the tractability of the approach in practice, and providing better estimates.

SuMa: Efficient LiDARbased Semantic SLAM 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 surfelbased mapping approach exploiting threedimensional 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 pointwise labels for the whole scan, allowing us to build a semanticallyenriched 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, stateoftheart approach.