Neural Semantic Map-Learning for Autonomous Vehicles

Autonomous vehicles demand detailed maps to maneuver reliably through traffic, which need to be kept up-to-date to ensure a safe operation. A promising way to adapt the maps to the ever-changing road-network is to use crowd-sourced data from a fleet of vehicles. In this work, we present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment including drivable area, lane markings, poles, obstacles and more as a 3D mesh. Each vehicle contributes locally reconstructed submaps as lightweight meshes, making our method applicable to a wide range of reconstruction methods and sensor modalities. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field, which is supervised using the submap meshes to predict a fused environment representation. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction. Our approach is evaluated on two datasets with different local mapping methods, showing improved pose alignment and reconstruction over existing methods. Additionally, we demonstrate the benefit of multi-session mapping and examine the required amount of data to enable high-fidelity map learning for autonomous vehicles.

Authors

• Markus Herb
• Nassir Navab
• Federico Tombari

Keywords

• Three-dimensional displays
• Uncertainty
• Fuses
• Roads
• Semantics
• Reconstruction algorithms
• Robot sensing systems
• Robustness
• Servers
• Autonomous vehicles
• Reconstruction Method
• Local Map
• 3D Mesh
• Mapping System
• Vehicle Fleet
• Crowdsourced Data
• Signed Distance Function
• Lane Markings
• Density Map
• Precision And Recall
• Semantic Information
• Mapping Data
• Multilayer Perceptron
• Semantic Segmentation
• Reconstruction Results
• Surface Reconstruction
• Backward Direction
• Semantic Labels
• Triangular Mesh
• Dense Reconstruction
• Neural Field
• Simultaneous Localization And Mapping
• Artificial Noise
• Map Reconstruction
• Surface Normals
• Large-scale Reconstruction
• GPS Measurements
• Stereo Camera
• Reconstruction Quality