SuMa++: Efficient LiDAR-based Semantic SLAM

TL;DR

SuMa++ enhances LiDAR-based SLAM by integrating semantic segmentation from neural networks into surfel mapping, improving robustness and accuracy in dynamic environments like highways with many moving objects.

Contribution

It introduces a semantic extension to surfel-based SLAM, enabling filtering of moving objects and improved scan matching using semantic constraints.

Findings

Outperforms purely geometric SLAM in dynamic highway environments.

Effectively filters moving objects using semantic labels.

Improves localization accuracy with semantic information.

Abstract

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.