Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset

Delmerico, Jeffrey A.; Cieslewski, Titus; Rebecq, Henri; Faessler, Matthias; Scaramuzza, Davide

doi:10.1109/icra.2019.8793887

Cited by 181 publications

(131 citation statements)

References 25 publications

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“…In addition the discussed datasets above, Table 3 presents a view for various multi-sensors agent navigation datasets, including UZH-FPV Drone Racing [ 125 ], TUM RGB-D Dataset [ 126 ], ScanNet [ 127 ], NYU V2 [ 128 ], InteriorNet [ 129 ], SceneNet RGB-D [ 130 ], and others [ 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 ], etc. These datasets provide the basic requirements of simulation and evaluation of multi-sensor fusion in experiments.…”

Section: Multi-modal Datasetsmentioning

confidence: 99%

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Yang

Zhang

et al. 2021

Sensors

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Indoor autonomous navigation refers to the perception and exploration abilities of mobile agents in unknown indoor environments with the help of various sensors. It is the basic and one of the most important functions of mobile agents. In spite of the high performance of the single-sensor navigation method, multi-sensor fusion methods still potentially improve the perception and navigation abilities of mobile agents. This work summarizes the multi-sensor fusion methods for mobile agents’ navigation by: (1) analyzing and comparing the advantages and disadvantages of a single sensor in the task of navigation; (2) introducing the mainstream technologies of multi-sensor fusion methods, including various combinations of sensors and several widely recognized multi-modal sensor datasets. Finally, we discuss the possible technique trends of multi-sensor fusion methods, especially its technique challenges in practical navigation environments.

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Section: Multi-modal Datasetsmentioning

confidence: 99%

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Yang

Zhang

et al. 2021

Sensors

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“…By bringing down expenses and providing reliable benchmarks, datasets as well as data simulators are elementary tools for further improvement in event-based vision. Sorted by task, they are generally divided into datasets for regression tasks [18][19][20][21] and those for classification tasks [22,23]. Simulators [24,25] [5][6][7], hardware (in yellow color) [8][9][10], and dataset (in green color) [11,12].…”

Section: A Glance Over Event-based Visionmentioning

confidence: 99%

Data‐Driven Technology in Event‐Based Vision

Sun

Shi²,

Zhang³

et al. 2021

Complexity

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Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware and datasets constitute an emerging field named event-based data-driven technology. Focusing on data-driven technology in event-based vision, this paper first explicates the operating principle, advantages, and intrinsic nature of event cameras, as well as background knowledge in event-based vision, presenting an overview of this research field. Then, we explain why event-based data-driven technology becomes a research focus, including reasons for the rise of event-based vision and the superiority of data-driven approaches over other event-based algorithms. Current status and future trends of event-based data-driven technology are presented successively in terms of hardware, datasets, and algorithms, providing guidance for future research. Generally, this paper reveals the great prospects of event-based data-driven technology and presents a comprehensive overview of this field, aiming at a more efficient and bio-inspired visual system to extract visual features from the external environment.

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“…In addition to the above toy examples, we compare the proposed eFMT with FMT, ORB-SLAM3, SVO and DSO on a bigger UAV dataset. Note that even though there are several public UAV datasets [40][41][42][43][44], we could not use them in this paper because we require datasets without roll/ pitch due to the properties of our algorithm. Ref.…”

Section: The Uav Datasetmentioning

confidence: 99%

Rethinking the Fourier-Mellin Transform: Multiple Depths in the Camera’s View

Kuang

Kneip

et al. 2021

Remote Sensing

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Remote sensing and robotics often rely on visual odometry (VO) for localization. Many standard approaches for VO use feature detection. However, these methods will meet challenges if the environments are feature-deprived or highly repetitive. Fourier-Mellin Transform (FMT) is an alternative VO approach that has been shown to show superior performance in these scenarios and is often used in remote sensing. One limitation of FMT is that it requires an environment that is equidistant to the camera, i.e., single-depth. To extend the applications of FMT to multi-depth environments, this paper presents the extended Fourier-Mellin Transform (eFMT), which maintains the advantages of FMT with respect to feature-deprived scenarios. To show the robustness and accuracy of eFMT, we implement an eFMT-based visual odometry framework and test it in toy examples and a large-scale drone dataset. All these experiments are performed on data collected in challenging scenarios, such as, trees, wooden boards and featureless roofs. The results show that eFMT performs better than FMT in the multi-depth settings. Moreover, eFMT also outperforms state-of-the-art VO algorithms, such as ORB-SLAM3, SVO and DSO, in our experiments.

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Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset

Cited by 181 publications

References 25 publications

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Data‐Driven Technology in Event‐Based Vision

Rethinking the Fourier-Mellin Transform: Multiple Depths in the Camera’s View

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