Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

Li, Tuan; Zhang, Hongping; Гао, Зонгшенг; Niu, Xiaoji; El‐Sheimy, Naser

doi:10.3390/rs11060610

Cited by 110 publications

(60 citation statements)

References 40 publications

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“…For GNSS-challenging environments, research has been conducted on methods to account for and overcome the degraded signal. Sensor fusion approaches using monocular or stereovision cameras alongside the Inertial Navigation System (INS) coupled with the GNSS signal have been tested by Li et al (2019) and Andert et al (2013). Other approaches have used multiple vehicles in communication with each other to improve navigation, using those vehicles under normal GNSS conditions to improve the position estimation of any vehicle with poor GNSS signal.…”

Section: Introductionmentioning

confidence: 99%

Visual Odometry Using Pixel Processor Arrays for Unmanned Aerial Systems in GPS Denied Environments

et al. 2020

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Environments in which Global Positioning Systems (GPS), or more generally Global Navigation Satellite System (GNSS), signals are denied or degraded pose problems for the guidance, navigation, and control of autonomous systems. This can make operating in hostile GNSS-Impaired environments, such as indoors, or in urban and natural canyons, impossible or extremely difficult. Pixel Processor Array (PPA) cameras-in conjunction with other on-board sensors-can be used to address this problem, aiding in tracking, localization, and control. In this paper we demonstrate the use of a PPA device-the SCAMP vision chip-combining perception and compute capabilities on the same device for aiding in real-time navigation and control of aerial robots. A PPA consists of an array of Processing Elements (PEs), each of which features light capture, processing, and storage capabilities. This allows various image processing tasks to be efficiently performed directly on the sensor itself. Within this paper we demonstrate visual odometry and target identification running concurrently on-board a single PPA vision chip at a combined frequency in the region of 400 Hz. Results from outdoor multirotor test flights are given along with comparisons against baseline GPS results. The SCAMP PPA's High Dynamic Range (HDR) and ability to run multiple algorithms at adaptive rates makes the sensor well suited for addressing outdoor flight of small UAS in GNSS challenging or denied environments. HDR allows operation to continue during the transition from indoor to outdoor environments, and in other situations where there are significant variations in light levels. Additionally, the PPA only needs to output specific information such as the optic flow and target position, rather than having to output entire images. This significantly reduces the bandwidth required for communication between the sensor and on-board flight computer, enabling high frame rate, low power operation.

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Section: Introductionmentioning

confidence: 99%

Visual Odometry Using Pixel Processor Arrays for Unmanned Aerial Systems in GPS Denied Environments

et al. 2020

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“…Such interference leads to an enormous positioning error, possibly exceeding 30 meters, which is unqualified for ITS [2,3]. Although various approaches, such as the integration of GNSS with INS, camera [4],or LiDAR-based simultaneous localization and mapping (SLAM) [5,6], are capable of achieving better positioning accuracy, they still require an accurate absolute positioning solution from GNSS for initialization. It is inevitable to improve the GNSS positioning accuracy in the urban area prior to ITS development.…”

Section: Introductionmentioning

confidence: 99%

GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

Zhang¹,

Xu²,

Ng³

et al. 2020

Preprint

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Accurate localization of road agents is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques are recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiment requiring numbers of devices is hard to be conducted, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

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“…For example, INS has a slowly varying drift, and the navigation error increases over time. 3,4 In visual navigation, the image information to be processed is large, so the real-time performance is poor, and it is easy to be constrained by weather factors such as rain and night. 5 Port AGV working area is generally large, when using lidar SLAM navigation, there are few reference signs, which is not conducive to stitching the map in real time.…”

Section: Introductionmentioning

confidence: 99%

An improved integrated navigation method based on RINS, GNSS and kinematics for port heavy-duty AGV

Feng

Duan³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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The current navigation methods for port heavy-duty automated guided vehicle mainly include the antenna radar-transponder navigation and the global navigation satellite system. However, the former has a huge cost and the latter will generate multi-path error easily. To avoid these problems, an improved integrated navigation method based on single-axis rotating inertial navigation system, global navigation satellite system and kinematics is proposed. First, the rotating inertial navigation system/ global navigation satellite system and rotating inertial navigation system/Kinematics integrated navigation methods generate corresponding estimates and filtering error covariances through their respective extended Kalman filter filters, and then the two sets of results are fused by the optimal weighted voting fusion method. The proposed method is applied to a heavy-duty automated guided vehicle for engineering verification. Without multi-path error, the navigation accuracy is 1.8–2.98 times higher than that of the traditional global navigation satellite system navigation. In the case of multi-path error, the improved method still has high fault tolerance and high navigation accuracy. The accuracy of this method satisfies the requirements of port heavy-duty automated guided vehicle, which can greatly reduce the number of transponders and has high practical value.

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Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

Cited by 110 publications

References 40 publications

Visual Odometry Using Pixel Processor Arrays for Unmanned Aerial Systems in GPS Denied Environments

Visual Odometry Using Pixel Processor Arrays for Unmanned Aerial Systems in GPS Denied Environments

GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

An improved integrated navigation method based on RINS, GNSS and kinematics for port heavy-duty AGV

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