Neural Semantic Map-Learning for Autonomous Vehicles

• URL: http://arxiv.org/abs/2410.07780v1
• Date: Thu, 10 Oct 2024 10:10:03 GMT
• Title: Neural Semantic Map-Learning for Autonomous Vehicles
• Authors: Markus Herb, Nassir Navab, Federico Tombari,
• Abstract summary: 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. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
• Score: 85.8425492858912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
  We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view. Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
  arXiv  Detail & Related papers  (2022-11-15T13:52:41Z)
• A Survey on Visual Map Localization Using LiDARs and Cameras [0.0]
  We define visual map localization as a two-stage process. At the stage of place recognition, the initial position of the vehicle in the map is determined by comparing the visual sensor output with a set of geo-tagged map regions of interest. At the stage of map metric localization, the vehicle is tracked while it moves across the map by continuously aligning the visual sensors' output with the current area of the map that is being traversed.
  arXiv  Detail & Related papers  (2022-08-05T20:11:18Z)
• Semantic Image Alignment for Vehicle Localization [111.59616433224662]
  We present a novel approach to vehicle localization in dense semantic maps using semantic segmentation from a monocular camera. In contrast to existing visual localization approaches, the system does not require additional keypoint features, handcrafted localization landmark extractors or expensive LiDAR sensors.
  arXiv  Detail & Related papers  (2021-10-08T14:40:15Z)
• Trajectory Prediction for Autonomous Driving with Topometric Map [10.831436392239585]
  State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. We propose an end-to-end transformer networks based approach for map-less autonomous driving.
  arXiv  Detail & Related papers  (2021-05-09T08:16:16Z)
• Convolutional Recurrent Network for Road Boundary Extraction [99.55522995570063]
  We tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. We design a structured model where a fully convolutional network obtains deep features encoding the location and direction of road boundaries. We showcase the effectiveness of our method on a large North American city where we obtain perfect topology of road boundaries 99.3% of the time.
  arXiv  Detail & Related papers  (2020-12-21T18:59:12Z)
• LiveMap: Real-Time Dynamic Map in Automotive Edge Computing [14.195521569220448]
  LiveMap is a real-time dynamic map that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the control plane of LiveMap that allows adaptive offloading of vehicle computations. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator.
  arXiv  Detail & Related papers  (2020-12-16T15:00:49Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)
• Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks [27.86228863466213]
  We present a simple, unified approach for estimating maps directly from monocular images using a single end-to-end deep learning architecture. We demonstrate the effectiveness of our approach by evaluating against several challenging baselines on the NuScenes and Argoverse datasets.
  arXiv  Detail & Related papers  (2020-03-30T12:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.