VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach

Nguyen, Thien‐Minh; Cao, Muqing; Yuan, Shenghai; Lyu, Yang; Xie, Lihua

doi:10.1109/tro.2021.3094157

“…The UWB ranging network is setup in a similar manner with our previous works Nguyen et al (2020bNguyen et al ( , 2021aNguyen et al ( , 2018. Two Humatics P440 UWB radios 6 , given the IDs 200 and 201 in the network, are mounted on the UAV, each has two antennae A and B extended to the four corners.…”

Section: Uwb Ranging Sensorsmentioning

confidence: 99%

NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial Vehicle Viewpoint

Nguyen,

Yuan,

Cao

et al. 2022

Preprint

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In recent years, autonomous robots have become ubiquitous in research and daily life. Among many factors, public datasets play an important role in the progress of this field, as they waive the tall order of initial investment in hardware and manpower. However, for research on autonomous aerial systems, there appears to be a relative lack of public datasets on par with those used for autonomous driving and ground robots. Thus, to fill in this gap, we conduct a data collection exercise on an aerial platform equipped with an extensive and unique set of sensors: two 3D lidars, two hardware-synchronized global-shutter cameras, multiple Inertial Measurement Units (IMUs), and especially, multiple Ultra-wideband (UWB) ranging units. The comprehensive sensor suite resembles that of an autonomous driving car, but features distinct and challenging characteristics of aerial operations. We record multiple datasets in several challenging indoor and outdoor conditions. Calibration results and ground truth from a high-accuracy laser tracker are also included in each package. All resources can be accessed via our webpage https://ntu-aris.github.io/ntu_viral_ dataset/.

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“…The UWB ranging network is set up in a similar manner with our previous works Nguyen et al (2020b, 2021c, 2018). Two Humatics P440 UWB radios 6 , given the IDs 200 and 201 in the network, are mounted on the UAV, each has two antennae A and B extended to the four corners.…”

Section: Sensor Setupmentioning

confidence: 99%

NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint

Yuan

¹

,

Cao

²

,

Lyu

³

et al. 2021

The International Journal of Robotics Research

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In recent years, autonomous robots have become ubiquitous in research and daily life. Among many factors, public datasets play an important role in the progress of this field, as they waive the tall order of initial investment in hardware and manpower. However, for research on autonomous aerial systems, there appears to be a relative lack of public datasets on par with those used for autonomous driving and ground robots. Thus, to fill in this gap, we conduct a data collection exercise on an aerial platform equipped with an extensive and unique set of sensors: two 3D lidars, two hardware-synchronized global-shutter cameras, multiple Inertial Measurement Units (IMUs), and especially, multiple Ultra-wideband (UWB) ranging units. The comprehensive sensor suite resembles that of an autonomous driving car, but features distinct and challenging characteristics of aerial operations. We record multiple datasets in several challenging indoor and outdoor conditions. Calibration results and ground truth from a high-accuracy laser tracker are also included in each package. All resources can be accessed via our webpage https://ntu-aris.github.io/ntu_viral_ dataset/ .

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“…Among tightly coupled frames, [5] [6] introduced an optimization-based approach combining IMU, camera, Li-DAR, and UWB measurements to estimate the trajectory of the mobile robot in a sliding window. [7] [8] proposed to integrate the information provided by 3D laser scan, UWB, and INS in a filter-based way such as error state Kalman filter (ESKF) or extend Kalman filter (EKF).…”

Section: Related Workmentioning

confidence: 99%

“…[7] [8] proposed to integrate the information provided by 3D laser scan, UWB, and INS in a filter-based way such as error state Kalman filter (ESKF) or extend Kalman filter (EKF). But [5] [6] [8] do not consider the degeneration situation and [7] cannot acquire the prior information of UWB automatically.…”

Section: Related Workmentioning

confidence: 99%

Anti-degenerated UWB-LiDAR Localization for Automatic Road Roller in Tunnel

Shen¹,

Chen²,

Yang³

et al. 2021

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The automatic road roller, as a popular type of construction robot, has attracted much interest from both the industry and the research community in recent years. However, when it comes to tunnels where the degeneration issues are prone to happen, it is still a challenging problem to provide an accurate positioning result for the robot. In this paper, we aim to deal with this problem by fusing LiDAR and UWB measurements based on optimization. In the proposed localization method, the directions of non-degeneration will be constrained and the covariance of UWB reconstruction will be introduced to improve the accuracy of localization. Apart from these, a method that can extract the feature of the inner wall of tunnels to assist positioning is also presented in this paper. To evaluate the effectiveness of the proposed method, three experiments with real road roller were carried out and the results show that our method can achieve better performance than the existing methods and can be applied to automatic road roller working inside tunnels. Finally, we discuss the feasibility of deploying the system in real applications and make several recommendations.

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VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach

Cited by 71 publications

References 43 publications

NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial Vehicle Viewpoint

NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial Vehicle Viewpoint

NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint

Anti-degenerated UWB-LiDAR Localization for Automatic Road Roller in Tunnel

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