SegMap: 3D Segment Mapping using Data-Driven Descriptors

Abstract

When performing localization and mapping, working at the level of structure can be advantageous in terms of robustness to environmental changes and differences in illumination. This paper presents SegMap: a map representation solution to the localization and mapping problem based on the extraction of segments in 3D point clouds. In addition to facilitating the computationally intensive task of processing 3D point clouds, working at the level of segments addresses the data compression requirements of real-time single- and multi-robot systems. While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information. This is particularly interesting for navigation tasks and for providing visual feedback to end-users such as robot operators, for example in search and rescue scenarios. These capabilities are demonstrated in multiple urban driving and search and rescue experiments. Our method leads to an increase of area under the ROC curve of 28.3% over current state of the art using eigenvalue based features. We also obtain very similar reconstruction capabilities to a model specifically trained for this task. The SegMap implementation will be made available open-source along with easy to run demonstrations at www.github.com/ethz-asl/segmap. A video demonstration is available at https://youtu.be/CMk4w4eRobg.

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Cited by in corpus (15)

• SuMa++: Efficient LiDAR-based Semantic SLAM
• SegMap: Segment-based mapping and localization using data-driven descriptors
• OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios
• Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
• LiDAR-Based Place Recognition For Autonomous Driving: A Survey
• Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation
• Challenges of SLAM in extremely unstructured environments: the DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset
• Multi S-Graphs: An Efficient Distributed Semantic-Relational Collaborative SLAM
• AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition
• 3DEG: Data-Driven Descriptor Extraction for Global re-localization in subterranean environments
• Robust Imaging Sonar-based Place Recognition and Localization in Underwater Environments
• LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks
• A Survey on 3D LiDAR Localization for Autonomous Vehicles
• Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps
• LAMP: Implicit Language Map for Robot Navigation