SuMa++: Efficient LiDAR-based Semantic SLAM

By Xieyuanli Chen, Andres Milioto Emanuele Palazzolo, Philippe Giguère, Jens Behley, Cyrill Stachniss. IEEE/RJS International Conference on Intelligent RObots and Systems, 2019

555 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/546137c71dc3c00c8a259de721281930f6574086

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Cyrill Stachniss
2. Jens Behley
3. Ignacio Vizzo
4. Tiziano Guadagnino
5. Louis Wiesmann
6. Benedikt Mersch

In Computer Vision and Pattern Recognition

1. Jens Behley
2. Cyrill Stachniss
3. Ignacio Vizzo
4. Tiziano Guadagnino
5. Louis Wiesmann
6. Benedikt Mersch

In Environmental Engineering

1. Cyrill Stachniss
2. Jens Behley
3. Ignacio Vizzo
4. Tiziano Guadagnino
5. Louis Wiesmann
6. Benedikt Mersch

Citations in the corpus

• KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way (2022, background): “It has also been extended to account for semantics [8] and explicitly handle dynamic objects [7].”
• SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation (2020, unknown): “2D/3D object detection [65,3,41,34], multi-modal fusion [64,17], simultaneous localization and mapping [4,2] and point-cloud segmentation [56,58,35].”