Encoder-Camera-Ground Penetrating Radar Tri-Sensor Mapping for Surface and Subsurface Transportation Infrastructure Inspection

Chou, Chieh; Kingery, Aaron; Wang, Di; Li, Haifeng; Song, Dezhen

doi:10.1109/icra.2018.8461080

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…The similarity S P 1 /VO T p 1 relative to the reference point T p 1 between P 1 and the position of VO is calculated using Equation (5).…”

Section: S = Expmentioning

confidence: 99%

“…However, due to the inherent shortcomings of satellite positioning principles, the global satellite positioning system has problems, such as unstable signals and low local positioning accuracy in urban canyon environments [3,4]. Inertial sensor [5][6][7] or wheel odometry [8][9][10] data are usually introduced into the positioning system to provide accurate and robust vehicle positioning information. But the external parameters between different sensors in the multi-sensor fusion system are easily affected by mechanical deformation and harsh environments, which will lead to a reduction in system positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization

Xu,

Sun,

Zhao

et al. 2023

Sensors

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Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem. However, visual odometry relies on dead-reckoning technology, where a slight positioning error can accumulate over time, resulting in a catastrophic positioning error. Thus, this paper proposes a road-network-map-assisted vehicle positioning method based on the theory of pose graph optimization. This method takes the dead-reckoning result of visual odometry as the input and introduces constraints from the point-line form road network map to suppress the accumulated error and improve vehicle positioning accuracy. We design an optimization and prediction model, and the original trajectory of visual odometry is optimized to obtain the corrected trajectory by introducing constraints from map correction points. The vehicle positioning result at the next moment is predicted based on the latest output of the visual odometry and corrected trajectory. The experiments carried out on the KITTI and campus datasets demonstrate the superiority of the proposed method, which can provide stable and accurate vehicle position estimation in real-time, and has higher positioning accuracy than similar map-assisted methods.

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“…The similarity S P 1 /VO T p 1 relative to the reference point T p 1 between P 1 and the position of VO is calculated using Equation (5).…”

Section: S = Expmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization

Xu,

Sun,

Zhao

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…GPS receiver [8], [9] is another commonly-used sensor but it malfunctions when the vehicle travels close to high-rise buildings or inside tunnels. On the other hand, proprioceptive sensors, such as IMUs [10] and wheel encoders [11], are inherently immune to external conditions. However, they are more susceptible to error drift and suffer from limited accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Combining ( 9), ( 8), (11), and (10), we complete the observation model functions. The rest is to follow the standard EKF setup.…”

Section: And the Euler Anglesmentioning

confidence: 99%

Graph-based Proprioceptive Localization Using a Discrete Heading-Length Feature Sequence Matching Approach

Cheng

Song

2020

Preprint

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Proprioceptive localization refers to a new class of robot egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods are naturally immune to bad weather, poor lighting conditions, or other extreme environmental conditions that may hinder exteroceptive sensors such as a camera or a laser ranger finder. These methods depend on proprioceptive sensors such as inertial measurement units (IMUs) and/or wheel encoders. Assisted by magnetoreception, the sensors can provide a rudimentary estimation of vehicle trajectory which is used to query a prior known map to obtain location. Named as graph-based proprioceptive localization (GBPL), we provide a low cost fallback solution for localization under challenging environmental conditions. As a robot/vehicle travels, we extract a sequence of heading-length values for straight segments from the trajectory and match the sequence with a pre-processed heading-length graph (HLG) abstracted from the prior known map to localize the robot under a graph-matching approach. Using the information from HLG, our location alignment and verification module compensates for trajectory drift, wheel slip, or tire inflation level. We have implemented our algorithm and tested it in both simulated and physical experiments. The algorithm runs successfully in finding robot location continuously and achieves localization accurate at the level that the prior map allows (less than 10m).

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