SuMa++: Efficient LiDAR-based Semantic SLAM (2105.11320v1)

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.

• The paper integrates semantic labels via a convolutional network into a surfel-based LiDAR mapping approach, significantly improving SLAM accuracy.
• The paper refines projective scan matching and filters moving objects using semantic constraints, ensuring robust environmental mapping.
• The paper demonstrates enhanced odometry quality and mapping reliability on dynamic highway sequences compared to geometric-only approaches.

SuMa++: Efficient LiDAR-based Semantic SLAM

The paper "SuMa++: Efficient LiDAR-based Semantic SLAM" introduces an advanced simultaneous localization and mapping (SLAM) framework that integrates semantic information into LiDAR-based mapping. The authors aim to enhance the accuracy and reliability of SLAM in dynamic environments by incorporating a fully convolutional network to extract semantic labels, which are utilized to improve the mapping process.

Key Contributions

The main contributions of this research include:

1. Integration of Semantic Information: The paper proposes incorporating semantic information into a previously introduced surfel-based mapping approach, which utilizes three-dimensional laser range scans. The semantic data is generated through a fully convolutional neural network and significantly enhances the accuracy of the mapped environments by providing semantic segmentation of the entire scan.
2. Improved Mapping via Semantic Constraints: By leveraging semantic segmentation, the authors enriched maps with labeled surfels, which allow for the filtering of moving objects and refinement of projective scan matching.
3. Dynamic Object Filtering: The semantic SLAM pipeline effectively filters moving objects by cross-referencing semantic consistency between new observations and the existing map model, reducing the incorporation of dynamic obstacles into the map, thereby enhancing mapping reliability in dynamic scenarios.

Experimental Results

The experiments conducted using challenging highway sequences from the KITTI dataset demonstrate that the proposed semantic SLAM approach, dubbed SuMa++, provides significant improvements over purely geometric SLAM approaches. Specifically, SuMa++ exhibits better performance in terms of both mapping accuracy and odometry quality, even in environments with minimal static features and numerous moving vehicles.

Implications and Future Directions

This research has significant practical implications for autonomous navigation systems operating in dynamic environments. By integrating semantic information into LiDAR-based SLAM systems, the accuracy of localization and the quality of mapping can be substantially enhanced, leading to more reliable autonomous systems that can navigate complex surroundings with greater intelligence.

Looking forward, the authors suggest potential advancements in AI and autonomous navigation technologies. Future developments might focus on improving the semantic segmentation to provide finer-grained semantic information such as lane structure and road types. Additionally, exploring the use of semantic information in loop closure detection could further improve map consistency over long distances and revisited paths.

Conclusion

The SuMa++ framework represents a significant step forward in SLAM methodology by leveraging semantic information to enhance map generation and localization accuracy. The integration of semantic labels into a LiDAR-based mapping process demonstrates the benefits of combining geometric and semantic data, providing a foundation for future advancements in intelligent navigation systems. The research sets a precedent for further exploration into combining deep learning techniques with classical SLAM approaches to address challenges posed by dynamic environments.