Estimating the pose of an unmanned aerial vehicle (UAV) or drone is a challenging task. It is useful for many applications such as navigation, surveillance, tracking objects on the ground, and 3D reconstruction. In this work, we present a LiDAR-camera-based relative pose estimation method between a drone and a ground vehicle, using a LiDAR sensor and a fisheye camera on the vehicle's roof and another fisheye camera mounted under the drone. The LiDAR sensor directly observes the drone and measures its position, and the two cameras estimate the relative orientation using indirect observation of the surrounding objects. We propose a dynamically adaptive kernel-based method for drone detection and tracking using the LiDAR. We detect vanishing points in both cameras and find their correspondences to estimate the relative orientation. Additionally, we propose a rotation correction technique by relying on the observed motion of the drone through the LiDAR. In our experiments, we were able to achieve very fast initial detection and real-time tracking of the drone. Our method is fully automatic.
Valeo und Schalter und Sensoren GmbH（法雷奥开关与传感器有限公司）；Valeo Vision Systems（法雷奥视觉系统公司）；Technische University, Chemnitz（开姆尼茨工业大学）
Near field depth estimation around a self driving car is an important function that can be achieved by four wide angle fisheye cameras having a field of view of over 180. Depth estimation based on convolutional neural networks (CNNs) produce state of the art results, but progress is hindered because depth annotation cannot be obtained manually. Synthetic datasets are commonly used but they have limitations. For instance, they do not capture the extensive variability in the appearance of objects like vehicles present in real datasets. There is also a domain shift while performing inference on n…
Real-Time Sense and Detect of Drones Using Deep Learning and Airborne LiDAR
Drone Systems Lab, School of Computing, Engineering, and Physical Sciences, University of the West of Scotland（无人机系统实验室，计算、工程与物理科学学院，西方苏格兰大学）；Centre for Robotics & Intelligent Systems, Department of Electronic & Computer Engineering, University of Limerick（机器人与智能系统中心，电子与计算机工程系，利默里克大学）；Hamburg Port Authority（汉堡港口管理局）
Reliable knowledge of road boundaries is critical for autonomous vehicle navigation. We propose a robust curb detection and filtering technique based on the fusion of camera semantics and dense lidar point clouds. The lidar point clouds are collected by fusing multiple lidars for robust feature detection. The camera semantics are based on a modified EfficientNet architecture which is trained with labeled data collected from onboard fisheye cameras. The point clouds are associated with the closest curb segment with $L_2$-norm analysis after projecting into the image space with the fisheye model…
This work presents a technique for localization of a smart infrastructure node, consisting of a fisheye camera, in a prior map. These cameras can detect objects that are outside the line of sight of the autonomous vehicles (AV) and send that information to AVs using V2X technology. However, in order for this information to be of any use to the AV, the detected objects should be provided in the reference frame of the prior map that the AV uses for its own navigation. Therefore, it is important to know the accurate pose of the infrastructure camera with respect to the prior map. Here we propose …
We propose a novel filter for segmenting the regions of interest from LiDAR 3D point cloud for multirotor aerial vehicles. It is specially targeted for real-time applications and works on sparse LiDAR point clouds without preliminary mapping. We use this filter as a crucial component of fast obstacle avoidance system for agriculture drone operating at low altitude. As the first step, each point cloud is transformed into a depth image and then identify places near to the vehicle (local maxima) by locating areas with high pixel densities. Afterwards, we merge the original depth image with identi…
Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR
HUN-REN SZTAKI Institute for Computer Science and Control（HUN-REN SZTAKI计算机科学与控制研究所）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Eötvös Loránd University (ELTE)（欧多立·洛朗大学）；KTH Royal Institute of Technology（皇家理工学院）
State Key Laboratory of Optical Instrumentation, Zhejiang University, China（浙江大学光学仪器国家重点实验室）；Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Germany（卡尔斯鲁厄理工学院人机与机器人研究所）
Aerial pixel-wise scene perception of the surrounding environment is an important task for UAVs (Unmanned Aerial Vehicles). Previous research works mainly adopt conventional pinhole cameras or fisheye cameras as the imaging device. However, these imaging systems cannot achieve large Field of View (FoV), small size, and lightweight at the same time. To this end, we design a UAV system with a Panoramic Annular Lens (PAL), which has the characteristics of small size, low weight, and a 360-degree annular FoV. A lightweight panoramic annular semantic segmentation neural network model is designed to…
As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour and are often used in applications with limited compute, bandwidth, and storage resources. We present a fast, lossless compression algorithm for LiDAR range and attribute scan sequences including multiple-return range, signal, reflectivity, and ambient infrared. Our algorithm -- dubbed "Jiffy" -- achieves substantial compression by exploiting spatiotemporal redundancy and sparsity. Speed is accomplished by maximizing use of sin…
Visibility-Aware Cooperative Tracking with Decentralized LiDAR-Based Aerial Swarms
Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）；School of Intelligent System Engineering, Sun Yat-sen University（中山大学智能系统工程学院）
Drones Guiding Drones: Cooperative Navigation of a Less-Equipped Micro Aerial Vehicle in Cluttered Environments
Multi-robot Systems Group, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague（多机器人系统组，控制系，电气工程系，捷克技术大学）
Robert Bosch Center for Cyberphysical Systems, Indian Institute of Science（印度科学理工学院罗伯特·博世网络物理系统中心）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Current trends in autonomous vehicles and their applications indicates an increasing need in positioning at low battery and compute cost. Lidars provide accurate localization at the cost of high compute and power consumption which could be detrimental for drones. Modern requirements for autonomous drones such as No-Permit-No-Takeoff (NPNT) and applications restricting drones to a corridor require the infrastructure to constantly determine the location of the drone. Ultra Wide Band Radios (UWB) fulfill such requirements and offer high precision localization and fast position update rates at a f…
In this paper, we present a multi-camera visual odometry (VO) system for an autonomous vehicle. Our system mainly consists of a virtual LiDAR and a pose tracker. We use a perspective transformation method to synthesize a surround-view image from undistorted fisheye camera images. With a semantic segmentation model, the free space can be extracted. The scans of the virtual LiDAR are generated by discretizing the contours of the free space. As for the pose tracker, we propose a visual odometry system fusing both the feature matching and the virtual LiDAR scan matching results. Only those feature…
Universidad Tecnológica del Uruguay（乌拉圭技术大学）；Escuela Técnica Superior de Rivera（里韦拉高等技术学校）
Commercial drones are not yet dimensioned to perform indoor autonomous tasks, since they use GPS for their location in the environment. When it comes to a space with physical obstacles (walls, metal, etc.) between the communication of the drone and the satellites that allow the precise location of the same, there is great difficulty in finding the satellites or it generates interference for this location. This problem can cause an unexpected action of the drone, a collision and a possible accident can occur. The work to follow presents the development of a drone capable of operating in a physi…
We suggest the first system that runs real-time semantic segmentation via deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose price was \$15) attached to a toy-drone. In particular, since the Raspberry Pi weighs less than $16$ grams, and its size is half of a credit card, we could easily attach it to the common commercial DJI Tello toy-drone (<\$100, <90 grams, 98 $\times$ 92.5 $\times$ 41 mm). The result is an autonomous drone (no laptop nor human in the loop) that can detect and classify objects in real-time from a video stream of an on-board monocular RGB camera (…
ETH Zürich（苏黎世联邦理工学院）；inspire AG（Inspire股份公司）；Tinamu Labs（蒂纳穆实验室）
Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous systems using visual features for indoor localization and surface reconstruction from 2D-LiDAR measurements. A Gaussian Process-based model incorporates information collected from measurements given statistical prior information about the terrain, from which the volume estimate is computed. Our algorithm finds feasible trajectories which minimize the uncertainty of the volume estimate. We show results in simulation for th…
CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes
Department of Security and Crime Science, University College London（安全与犯罪科学系，伦敦大学学院）；Department of Computer Science, University College London（计算机科学系，伦敦大学学院）
University of Oxford（牛津大学）；Imperial College London（帝国理工学院）
We present an efficient, elastic 3D LiDAR reconstruction framework which can reconstruct up to maximum LiDAR ranges (60 m) at multiple frames per second, thus enabling robot exploration in large-scale environments. Our approach only requires a CPU. We focus on three main challenges of large-scale reconstruction: integration of long-range LiDAR scans at high frequency, the capacity to deform the reconstruction after loop closures are detected, and scalability for long-duration exploration. Our system extends upon a state-of-the-art efficient RGB-D volumetric reconstruction technique, called sup…
Oxford Robotics Institute（牛津机器人研究所）；University of Oxford（牛津大学）
Localization is a key challenge in many robotics applications. In this work we explore LIDAR-based global localization in both urban and natural environments and develop a method suitable for online application. Our approach leverages efficient deep learning architecture capable of learning compact point cloud descriptors directly from 3D data. The method uses an efficient feature space representation of a set of segmented point clouds to match between the current scene and the prior map. We show that down-sampling in the inner layers of the network can significantly reduce computation time wi…
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 …
Flying Robotics Art: ROS-based Drone Draws the Record-Breaking Mural
Lomonosov Moscow State University（莫斯科国立罗蒙诺索夫大学）；Copter Express Technologies Ltd.（Copter Express 技术有限公司）；Sverk Ltd.（Sverk 有限公司）；Cognitive Pilot Ltd.（Cognitive Pilot 有限公司）
In robotic applications, the control, and actuation deal with a continuous description of the system and environment, while high-level planning usually works with a discrete description. This paper considers the problem of bridging the low-level control and high-level planning for robotic systems via sensor data. In particular, we propose a discretization algorithm that identifies free polytopes via lidar point cloud data. A transition graph is then constructed where each node corresponds to a free polytope and two nodes are connected with an edge if the two corresponding free polytopes inters…
Safe autonomous landing in urban cities is a necessity for the growing Unmanned Aircraft Systems (UAS) industry. In urgent situations, building rooftops, particularly flat rooftops, can provide local safe landing zones for small UAS. This paper investigates the real-time identification and selection of safe landing zones on rooftops based on LiDAR and camera sensor feedback. A visual high fidelity simulated city is constructed in the Unreal game engine, with particular attention paid to accurately generating rooftops and the common obstructions found thereon, e.g., ac units, water towers, air …
University of Rochester（罗切斯特大学）；PerceptIn, Inc.（感知智能公司）
Compressing massive LiDAR point clouds in real-time is critical to autonomous machines such as drones and self-driving cars. While most of the recent prior work has focused on compressing individual point cloud frames, this paper proposes a novel system that effectively compresses a sequence of point clouds. The idea to exploit both the spatial and temporal redundancies in a sequence of point cloud frames. We first identify a key frame in a point cloud sequence and spatially encode the key frame by iterative plane fitting. We then exploit the fact that consecutive point clouds have large overl…
LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives
ShanghaiTech University（上海科技大学）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（上海先进研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；NeuDim Digital Technology (Shanghai) Co.,Ltd.（NeuDim数字技术（上海）有限公司）；DGene Digital Technology Co., Ltd.（DGene数字技术有限公司）
The University of Tokyo（东京大学）；NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室、加州理工学院）
Autonomous exploration of unknown environments with aerial vehicles remains a challenge, especially in perceptually degraded conditions. Dust, fog, or a lack of visual or LiDAR-based features results in severe difficulties for state estimation algorithms, which failure can be catastrophic. In this work, we show that it is indeed possible to navigate in such conditions without any exteroceptive sensing by exploiting collisions instead of treating them as constraints. To this end, we present a novel contact-based inertial odometry (CIO) algorithm: it uses estimated external forces with the envir…
Precision Meets Art: Autonomous Multi-UAV System for Large Scale Mural Drawing
Sverk Ltd.（Svek有限公司）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Cognitive Pilot Ltd.（Cognitive Pilot有限公司）；NUST MISIS（国立研究型技术大学莫斯科国立钢铁合金学院）；Kosygin State University of Russia（俄罗斯柯西金国立大学）
AeroTraj: Trajectory Planning for Fast, and Accurate 3D Reconstruction Using a Drone-based LiDAR
Rochester Institute of Technology（罗切斯特理工学院）；University of Southern California（南加州大学）；Nokia Bell Labs（诺基亚贝尔实验室）；Georgia Institute of Technology（佐治亚理工学院）
Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit
DGA Techniques Navales - Direction Générale de l’Armement（法国国防总局海军技术部）；LIRMM, TETIS, CNRS, Université de Montpellier Paul-Valéry（蒙彼利埃大学保罗-瓦莱里耶大学）；LIS, CNRS, Université de Toulon（土伦大学）
The University of Hong Kong（香港大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Accurate self and relative state estimation are the critical preconditions for completing swarm tasks, e.g., collaborative autonomous exploration, target tracking, search and rescue. This paper proposes Swarm-LIO: a fully decentralized state estimation method for aerial swarm systems, in which each drone performs precise ego-state estimation, exchanges ego-state and mutual observation information by wireless communication, and estimates relative state with respect to (w.r.t.) the rest of UAVs, all in real-time and only based on LiDAR-inertial measurements. A novel 3D LiDAR-based drone detectio…
Virtual Roads, Smarter Safety: A Digital Twin Framework for Mixed Autonomous Traffic Safety Analysis
Zachry Department of Civil and Environmental Engineering, Texas A&M University（土木与环境工程系，德克萨斯A&M大学）；Department of Landscape Architecture and Urban Planning（景观建筑与城市规划系）
