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Fast flights with aggressive maneuvers in cluttered GNSS-denied environments require fast, reliable, and accurate UAV state estimation. In this paper, we present an approach for onboard state estimation of a high-speed UAV using a monocular…

机器人学 · 计算机科学 2026-02-03 Filip Novák , Matěj Petrlík , Matej Novosad , Parakh M. Gupta , Robert Pěnička , Martin Saska

Accurate Angle-of-arrival (AoA) estimation is essential for next-generation wireless communication systems to enable reliable beamforming, high-precision localization, and integrated sensing. Unfortunately, classical high-resolution…

信号处理 · 电气工程与系统科学 2025-10-31 Shih-Kai Chou , Mengran Zhao , Cheng-Nan Hu , Kuang-Chung Chou , Carolina Fortuna , Jernej Hribar

Soft-actuated insect-scale micro aerial vehicles (IMAVs) pose unique challenges for designing robust and computationally efficient controllers. At the millimeter scale, fast robot dynamics ($\sim$ms), together with system delay, model…

机器人学 · 计算机科学 2025-02-19 Yi-Hsuan Hsiao , Wei-Tung Chen , Yun-Sheng Chang , Pulkit Agrawal , YuFeng Chen

In this paper, we propose a vision-based solution for indoor Micro Air Vehicle (MAV) navigation, with a primary focus on its application within autonomous warehouses. Our work centers on the utilization of a single camera as the primary…

机器人学 · 计算机科学 2023-10-12 Ling Shuang Soh , Hann Woei Ho

Real-time six degree-of-freedom pose estimation with ground vehicles represents a relevant and well studied topic in robotics, due to its many applications, such as autonomous driving and 3D mapping. Although some systems exist already,…

机器人学 · 计算机科学 2021-09-14 Matteo Frosi , Matteo Matteucci

Regression-based LiDAR relocalization has recently emerged as a promising solution for high-precision positioning in GNSS-denied environments. However, these methods are primarily tailored to autonomous driving, exhibiting significantly…

计算机视觉与模式识别 · 计算机科学 2026-05-13 Hengyu Mu , Jianshi Wu , Yuxin Guo , XianLian Lin , Qingyong Hu , Sheng Ao , Chenglu Wen , Cheng Wang

Automotive synthetic aperture radar (SAR) systems are rapidly emerging as a candidate technological solution to enable a high-resolution environment mapping for autonomous driving. Compared to lidars and cameras, automotive-legacy radars…

Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each…

Precise and real-time rail vehicle localization as well as railway environment monitoring is crucial for railroad safety. In this letter, we propose a multi-LiDAR based simultaneous localization and mapping (SLAM) system for railway…

机器人学 · 计算机科学 2021-12-28 Yusheng Wang , Weiwei Song , Yidong Lou , Fei Huang , Zhiyong Tu , Shimin Zhang

In this paper we address the problem of dot painting on a wall by a quadrotor Micro Air Vehicle (MAV), using on-board low cost sensors (monocular camera and IMU) for localization. A method is proposed to cope with uncertainties on the…

机器人学 · 计算机科学 2019-06-18 Thomas Chaffre , Kevin Tudal , Sylvain Bertrand , Lionel Prevost

Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation. However, modern SLAM frameworks often lead to unacceptably…

Aerial mapping systems are important for many surveying applications (e.g., industrial inspection or agricultural monitoring). Aerial platforms that can fly GPS-guided preplanned missions semi-autonomously are already widely available but…

机器人学 · 计算机科学 2024-09-18 Rowan Border , Nived Chebrolu , Yifu Tao , Jonathan D. Gammell , Maurice Fallon

The robust estimation of the mounting angle for millimeter-wave automotive radars installed on moving vehicles is investigated. We propose a novel signal processing pipeline that combines radar and inertial measurement unit (IMU) data to…

信号处理 · 电气工程与系统科学 2026-02-02 Simin Zhu , Satish Ravindran , Lihui Chen , Alexander Yarovoy , Francesco Fioranelli

The LiDAR and inertial sensors based localization and mapping are of great significance for Unmanned Ground Vehicle related applications. In this work, we have developed an improved LiDAR-inertial localization and mapping system for…

机器人学 · 计算机科学 2023-01-02 Kangcheng Liu

This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A model learning-based approach is proposed that takes in the rotor RPMs and past state as input and…

机器人学 · 计算机科学 2022-12-06 Nishanth Rao , Suresh Sundaram , Varun Raghavendra

Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This paper proposes a system to achieve…

机器人学 · 计算机科学 2021-05-06 Jianhao Jiao , Haoyang Ye , Yilong Zhu , Ming Liu

Efficient UAV exploration in unknown environments requires rapid coverage expansion while maintaining accurate and reliable localization, since safe navigation in complex scenes depends on consistent mapping and pose estimation. However,…

机器人学 · 计算机科学 2026-05-15 Jianping Li , Pengfei Wan , Zhongyuan Liu , Yi Wang , Yiheng Chen , Xinhang Xu , Rui Jin , Boyu Zhou , Lihua Xie

This work establishes COMPRA, a compact and reactive autonomy framework for fast deployment of Micro Aerial Vehicles (MAVs) in subterranean Search-and-Rescue (SAR) missions. A COMPRA-enabled MAV is able to autonomously explore previously…

This paper presents a family of autonomous Unmanned Aerial Vehicles (UAVs) platforms designed for a diverse range of indoor and outdoor applications. The proposed UAV design is highly modular in terms of used actuators, sensor…

Navigating unmanned aerial vehicles (UAVs) through cluttered and dynamic environments remains a significant challenge, particularly when dealing with fast-moving or sudden-appearing obstacles. This paper introduces a complete LiDAR-based…

机器人学 · 计算机科学 2025-04-25 Huajie Wu , Wenyi Liu , Yunfan Ren , Zheng Liu , Hairuo Wei , Fangcheng Zhu , Haotian Li , Fu Zhang