Infrastructure Node-based Vehicle Localization for Autonomous Driving

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1. Proche Pas pertinent

   FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles

   Vineet Gokhale, Gerardo Moyers Barrera, R. Venkatesha Prasad · 2021 · Robotique · Résumé IA disponible

   Résumé scientifique

   Autonomous vehicles have created a sensation in both outdoor and indoor applications. The famous indoor use-case is process automation inside a warehouse using Autonomous Indoor Vehicles (AIV). These vehicles need to locate themselves not only with an accuracy of a few centimetres but also within a few milliseconds in an energy-efficient manner. Due to these challenges, localization is a holy grail. In this paper, we propose FEEL - an indoor localization system that uses a fusion of three low-energy sensors: IMU, UWB, and radar. We provide detailed software and hardware architecture of FEEL. Further, we propose Adaptive Sensing Algorithm (ASA) for opportunistically minimizing energy consumpt…

2. Proche Pas pertinent

   Lidar SLAM for Autonomous Driving Vehicles

   Farhad Aghili · 2022 · Robotique · Résumé IA disponible

   Résumé scientifique

   This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the observability of the system are investigated. In addition to the vehicle's states and landmark positions, a self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix, and the observation sensitivity matrix are derived in closed-form making them suitable for real-time implementation. Examining the observability of the 3D SL…

3. Proche Pas pertinent

   Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments

   Shibo Zhao, Hengrui Zhang, Peng Wang et al. · 2021 · Robotique · Résumé IA disponible

   Résumé scientifique

   We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, visual-inertial odometry, and laser-inertial odometry. The visual-inertial odometry and laser-inertial odometry provide the pose p…

4. Apparenté Pas pertinent

   Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories

   Hanlin Chen, Renyuan Luo, Yiheng Feng · 2023 · Robotique · Résumé IA disponible

   Résumé scientifique

   Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping include high definition map (HD map) or real-time Simultaneous Localization and Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or embedded maps) and can not adapt well to temporarily changed drivable areas such as work zones. Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information. Difficulties in improving perception accuracy and ensuring the correct interpretation of perception results are challenging to the vehicle in these situations. This paper presents a prototype that introduces crowdsourcing trajectories information into the …

5. Apparenté Pas pertinent

   Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception

   Junwon Seo, Jungwi Mun, Taekyung Kim · 2023 · Robotique · Résumé IA disponible

   Résumé scientifique

   Uncertainty in control and perception poses challenges for autonomous vehicle navigation in unstructured environments, leading to navigation failures and potential vehicle damage. This paper introduces a framework that minimizes control and perception uncertainty to ensure safe and reliable navigation. The framework consists of two uncertainty-aware models: a learning-based vehicle dynamics model and a self-supervised traversability estimation model. We train a vehicle dynamics model that can quantify the epistemic uncertainty of the model to perform active exploration, resulting in the efficient collection of training data and effective avoidance of uncertain state-action spaces. In additio…

6. Apparenté Pas pertinent

   AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education

   Tanmay Vilas Samak, Chinmay Vilas Samak, Sivanathan Kandhasamy et al. · 2022 · MDPI Robotics vol. 12, no. 3: 77, 2023 · Résumé IA disponible

   Résumé scientifique

   Prototyping and validating hardware-software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt towards developing such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks…

7. Apparenté Pas pertinent

   Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

   Alexander Schaefer, Daniel Büscher, Johan Vertens et al. · 2019 · European Conference on Mobile Robots, Prague, Czech Republic, 2019, pp. 1-7 · Résumé IA disponible

   Résumé scientifique

   Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation at www.github.com/acschaefer/polex. In extensive experiments, we demonstrate that our method improves on the state of the art with respect to long-term reliability and accuracy: First, we prove reliability by tasking the sys…

8. Apparenté Pas pertinent

   Autonomous Vehicles: Open-Source Technologies, Considerations, and Development

   Oussama Saoudi, Ishwar Singh, Hamidreza Mahyar · 2022 · Robotique · Résumé IA disponible

   Résumé scientifique

   Autonomous vehicles are the culmination of advances in many areas such as sensor technologies, artificial intelligence (AI), networking, and more. This paper will introduce the reader to the technologies that build autonomous vehicles. It will focus on open-source tools and libraries for autonomous vehicle development, making it cheaper and easier for developers and researchers to participate in the field. The topics covered are as follows. First, we will discuss the sensors used in autonomous vehicles and summarize their performance in different environments, costs, and unique features. Then we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for each modality. Third, …