Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation
Woven by Toyota, Inc.（丰田 woven 公司）；University of Bonn（波恩大学）；Center for Robotics, University of Bonn（波恩大学机器人中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
Balázs Opra, Betty Le Dem, Jeffrey M. Walls, Dimitar Lukarski, Cyrill Stachniss
地图对于车辆导航和自主机器人等多种应用都至关重要。这些应用都需要空间模型来实现有效的路径规划与定位。本文解决自动驾驶车辆的道路图构建挑战。尽管近期已有进展，创建道路图仍然耗费人力，且尚未实现完全自动化。本文的目标是自动且准确地生成此类图。现代汽车配备了用于当前高级驾驶辅助系统（如车道保持）的车载传感器。我们提出利用从消费级车辆的这些标准传感器获取的全球导航卫星系统（GNSS）轨迹和基础图像数据，以最少的工作量估计道路级地图。我们通过将该问题构建为使用卷积神经网络的道路中心线语义分割任务，来利用数据中的空间信息。我们还利用数据的时间序列特性，通过地图匹配来细化神经网络的输出。我们使用一支真实消费级车队实施并评估了所提方法，且仅使用已部署的车载传感器。评估表明，我们的方法不仅在较简单的道路配置上与现有方法相当，而且在更复杂的道路几何结构和拓扑上显著优于它们。本工作获得了 2023 年 Woven by Toyota Invention Award。
英文摘要：
Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This paper addresses the challenge of road graph construction for autonomous vehicles. Despite recent advances, creating a road graph remains labor-intensive and has yet to achieve full automation. The goal of this paper is to generate such graphs automatically and accurately. Modern cars are equipped with onboard sensors used for today's advanced driver assistance systems like lane keeping. We propose using global navigation satellite system (GNSS) traces and basic image data acquired from these standard sensors in consumer vehicles to estimate road-level maps with minimal effort. We exploit the spatial information in the data by framing the problem as a road centerline semantic segmentation task using a convolutional neural network. We also utilize the data's time series nature to refine the neural network's output by using map matching. We implemented and evaluated our method using a fleet of real consumer vehicles, only using the deployed onboard sensors. Our evaluation demonstrates that our approach not only matches existing methods on simpler road configurations but also significantly outperforms them on more complex road geometries and topologies. This work received the 2023 Woven by Toyota Invention Award.
Swift Navigation（Swift Navigation公司）；Xona Space Systems（Xona空间系统公司）
This paper surveys a number of recent developments in modern Global Navigation Satellite Systems (GNSS) and investigates the possible impact on autonomous driving architectures. Modern GNSS now consist of four independent global satellite constellations delivering modernized signals at multiple civil frequencies. New ground monitoring infrastructure, mathematical models, and internet services correct for errors in the GNSS signals at continent scale. Mass-market automotive-grade receiver chipsets are available at low Cost, Size, Weight, and Power (CSWaP). The result is that GNSS in 2020 delive…
Polytechnic University of Madrid（马德里理工大学）；Boeing Research & Technology Europe (BR&TE)（波音欧洲研究与技术部）
Autonomous unmanned aerial vehicle (UAV) inertial navigation exhibits an extreme dependency on the availability of global navigation satellite systems (GNSS) signals, without which it incurs in a slow but unavoidable position drift that may ultimately lead to the loss of the platform if the GNSS signals are not restored or the aircraft does not reach a location from which it can be recovered by remote control. This article describes an stochastic high fidelity simulation of the flight of a fixed wing low SWaP (size, weight, and power) autonomous UAV in turbulent and varying weather intended to…
Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming
Department of Engineering, University of Perugia（工程系，佩鲁吉亚大学）；Multi-Robot Systems Group, Faculty of Electrical Engineering, Czech Technical University in Prague（多机器人系统组，电气工程系，布拉格捷克技术大学）；D-ITET Department, ETH Zürich（D-ITET部门，苏黎世联邦理工学院）
Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理空间研究 institute FGI，芬兰国家土地测绘局）
A Practical-Driven Framework for Transitioning Drive-by-Wire to Autonomous Driving Systems: A Case Study with a Chrysler Pacifica Hybrid Vehicle
Durham School of Architectural Engineering and Construction University of Nebraska-Lincoln（达勒姆建筑工程与建设学院 俄克拉荷马州立大学）；Department of Computer Science University of Nebraska-Omaha（计算机科学系 俄克拉荷马州立大学）
Impact of Localization Errors on Label Quality for Online HD Map Construction
Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT)（测量与控制系统研究所，卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（信息技术研究所以）
