Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstructions from a large-scale object using SfM, there are many critical constraints on the quality of image data, which often become sources of inaccuracy as the current 3D reconstruction pipelines do not facilitate the users to determine the fidelity of input data during the image acquisition. In this paper, we present and advocate a closed-loop interactive approach that performs incremental reconstruction in real-time and gives users an online feedback about the quality parameters like Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We also propose a novel multi-scale camera network design to prevent scene drift caused by incremental map building, and release the first multi-scale image sequence dataset as a benchmark. Further, we evaluate our system on real outdoor scenes, and show that our interactive pipeline combined with a multi-scale camera network approach provides compelling accuracy in multi-view reconstruction tasks when compared against the state-of-the-art methods.
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）
In this paper, we present a real-time approach to local trajectory replanning for microaerial vehicles (MAVs). Current trajectory generation methods for multicopters achieve high success rates in cluttered environments, but assume that the environment is static and require prior knowledge of the map. In the presented study, we use the results of such planners and extend them with a local replanning algorithm that can handle unmodeled (possibly dynamic) obstacles while keeping the MAV close to the global trajectory. To ensure that the proposed approach is real-time capable, we maintain informat…
MAVLab, Department of Control and Operations, TU Delft（代尔夫特理工大学控制与运筹系MAV实验室）
This paper discusses a low-cost, open-source and open-hardware design and performance evaluation of a low-speed, multi-fan wind system dedicated to micro air vehicle (MAV) testing. In addition, a set of experiments with a flapping wing MAV and rotorcraft is presented, demonstrating the capabilities of the system and the properties of these different types of drones in response to various types of wind. We performed two sets of experiments where a MAV is flying into the wake of the fan system, gathering data about states, battery voltage and current. Firstly, we focus on steady wind conditions …
The great promises of neuromorphic sensing and processing for robotics have led researchers and engineers to investigate novel models for robust and reliable control of autonomous robots (navigation, obstacle detection and avoidance, etc.), especially for quadrotors in challenging contexts such as drone racing and aggressive maneuvers. Using spiking neural networks, these models can be run on neuromorphic hardware to benefit from outstanding update rates and high energy efficiency. Yet, low-level controllers are often neglected and remain outside of the neuromorphic loop. Designing low-level n…
Robotics, TCS Research \& Innovation, Kolkata, India
Autonomous Micro Aerial Vehicles (MAVs) gained tremendous attention in recent years. Autonomous flight in indoor requires a dense depth map for navigable space detection which is the fundamental component for autonomous navigation. In this paper, we address the problem of reconstructing dense depth while a drone is hovering (small camera motion) in indoor scenes using already estimated cameras and sparse point cloud obtained from a vSLAM. We start by segmenting the scene based on sudden depth variation using sparse 3D points and introduce a patch-based local plane fitting via energy minimizati…
Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Zhejianglab（之江实验室）；Zhejiang University（浙江大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Software, Tsinghua University（清华大学软件学院）；Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；RISC-V International Open Source Laboratory（RISC-V国际开源实验室）
On Adversarial Attacks In Acoustic Drone Localization
Department of Computer Science（计算机科学系）；Technion – Israel Institute of Technology（技术ion-以色列理工学院）；School of Electrical and Computer Engineering（电气与计算机工程学院）；Ben-Gurion University of the Negev（贝内尔谢瓦大学）；Institute of Science and Technology Austria（奥地利科学与技术研究院）
From Bench to Flight: Translating Drone Impact Tests into Operational Safety Limits
Lab INIT Robots, Department of Mechanical Engineering, École de technologie supérieure, Montreal, QC, Canada（Lab INIT Robots，机械工程系，École de technologie supérieure，蒙特利尔，QC，加拿大）
Multi-Task Reinforcement Learning of Drone Aerobatics by Exploiting Geometric Symmetries
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；WINDY Lab, School of Engineering, Westlake University（西湖大学工程学院WINDY实验室）；School of Automation Science and Electrical Engineering, Beihang University（北航自动化科学与电气工程学院）
Limited power and computational resources, absence of high-end sensor equipment and GPS-denied environments are challenges faced by autonomous micro areal vehicles (MAVs). We address these challenges in the context of autonomous navigation and landing of MAVs in indoor environments and propose a vision-based control approach using Supervised Learning. To achieve this, we collected data samples in a simulation environment which were labelled according to the optimal control command determined by a path planning algorithm. Based on these data samples, we trained a Convolutional Neural Network (C…
Micro Air Vehicles (MAVs) will unlock their true potential once they can operate in groups. To this end, it is essential for them to estimate on-board the relative location of their neighbors. The challenge lies in limiting the mass and processing burden needed to enable this. We developed a relative localization method that only requires the MAVs to communicate via their wireless transceiver. Communication allows the exchange of on-board states (velocity, height, and orientation), while the signal-strength provides range data. These quantities are fused to provide a full relative location est…
Delft University of Technology（代尔夫特理工大学）；Micro Air Vehicle lab, Control and Simulation, Delft University of Technology（代尔夫特理工大学控制与仿真系微型飞行器实验室）
Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defined sequence. In this paper, we develop an autonomous system for drones to race fully autonomously using only onboard resources. Instead of commonly used visual navigation methods, such as simultaneous localization and mapping and visual inertial odometry, which are computationally expensive for micro aerial vehicles (MAVs), we developed the highly efficient snake gate detection algorithm for visual navigation, which …
Developing Smart MAVs for Autonomous Inspection in GPS-denied Constructions
College of Engineering, South China Agriculture University（华南农业大学工程学院）；College of Civil Aviation, Nanjing University of Aeronautics and Astronautics（南京航空航天大学民航学院）；College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）
