A New Path Planning Algorithm Based on GNSS Localization Error Map

Zhang, Guohao; Hsu, Li‐Ta

doi:10.33012/2017.15269

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…wherex GPS is the GPS output of the UAV's global position, andΔx is the estimate of the translation between two consecutive point clouds obtained using ICP as shown in (1). The final LiDAR measurement z L is a sum of the ICP estimate in the global frame and the previous filter position estimate.…”

Section: Kf Detailsmentioning

confidence: 99%

“…Emerging applications in unmanned aerial vehicles (UAVs) such as 3D modeling, filming, surveying, search and rescue, and delivering packages, involve flying in urban environments. In these scenarios, autonomously navigating a UAV has certain advantages such as optimizing flight paths and sensing and avoiding collisions . To enable such autonomous control, we need a continuous and reliable source for the UAVs' positioning.…”

Section: Introductionmentioning

confidence: 99%

“…In these scenarios, autonomously navigating a UAV has certain advantages such as optimizing flight paths and sensing and avoiding collisions. 1,2 To enable such autonomous control, we need a continuous and reliable source for the UAVs' positioning. In most cases, global navigation satellite system (GNSS) is primarily relied on for outdoor positioning.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive covariance estimation of LiDAR‐based positioning errors for UAVs

Shetty

Gao

2019

NAVIGATION

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Outdoor positioning for unmanned aerial vehicles (UAVs) commonly relies on global navigation satellite system (GNSS) signals, which might be reflected or blocked in urban areas. Thus, additional on‐board sensors such as light detection and ranging (LiDAR) are desirable to aid positioning. To fuse measurements from different sensors, it is important to accurately characterize the error covariance matrices of individual sensor measurements. We propose a novel method for adaptively estimating the LiDAR‐based positioning error covariance matrix based on the point cloud features surrounding the UAV. We model the position error as a multivariate Gaussian distribution and estimate its covariance matrix from individual surface and edge feature points. Simulations show that our model is more accurate than a distance‐based covariance matrix model. Furthermore, we conduct an outdoor experiment that fuses global positioning system (GPS) signals and LiDAR position measurements. We demonstrate a clear improvement in the UAV's global position estimation using our adaptive covariance matrix for LiDAR‐based measurements.

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Section: Kf Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive covariance estimation of LiDAR‐based positioning errors for UAVs

Shetty

Gao

2019

NAVIGATION

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“…Assuming the rule of perfect reflection, the multipath and/or NLOS affected pseudorange can be simulated using the 3D building models. The same simulation tool is used to generate a GPS positioning error map of an urban area [26]. In addition, the atmospheric delay and the random noise based on / and elevation [27] are added into the measurement, aiming to simulate realistic pseudorange measurements.…”

Section: A Simulationmentioning

confidence: 99%

A novel GNSS based V2V cooperative localization to exclude multipath effect using consistency checks

Zhang

Wen

Hsu

2018

2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Global Navigation Satellite System (GNSS) is essential for autonomous driving by providing absolute positioning solutions. However, the performance of GNSS measurement is significantly degraded in urban areas, due to the severe multipath and non-line-of-sight (NLOS) effects. To ensure the operation safety of autonomous driving applications, the GNSS localization performance is required to improve. In this study, a novel vehicle-to-vehicle (V2V) based cooperative localization algorithm with double-layers of consistency check (CC) is developed. GNSS pseudorange measurements of the ego-vehicle and surrounding vehicles conduct the first-layer of CC independently to exclude the multipath-biased measurement and obtaining their absolute positions. Then, the survived GNSS measurement and the absolute positions of each vehicles are shared in between, and further applied with double difference (DD) technique to obtain an accurate relative position between the surrounding vehicles. The second-layer CC is conducted during the GNSS DD-based relative positioning, to exclude the multipath and NLOS effects more comprehensively. Finally, the absolute and relative positions of surrounding vehicles are cooperated to optimize the final position of all participating vehicles. By the both simulation and real experimental results, the proposed algorithm is able to mitigate the multipath effect and significantly improve the accuracy of GNSS localization solutions to fulfill the safety requirements for autonomous driving.

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“…Regarding the 2D path planning, this paper uses A* path planning cooperating with a predicted GNSS localization error map and building model to plan an optimal path in an urban area. The first result is reported in [39]. By predicting the satellites' positions through almanac data and simulating signal reflection paths by a 3D building model and ray-tracing technique, the multipath effect and non-line-of-sight (NLOS) reception can be modeled.…”

Section: Introduction 30mentioning

confidence: 99%

A New Path Planning Algorithm Using a GNSS Localization Error Map for UAVs in an Urban Area

Zhang

Hsu

2018

J Intell Robot Syst

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The mission of future parcel delivery will be performed by unmanned aerial vehicles (UAVs). However, the localization of global navigation satellite systems (GNSS) in urban areas experiences the notorious multipath effect and non-line-of-sight (NLOS) reception, which could potentially generate approximately 50 meters of positioning error. This misleading localization result can be 12 hazardous for UAV applications in GNSS-challenged areas. Due to multipath complexity, there is 13 no general solution to eliminate this effect. A solution to guide UAV operation is to plan an optimal 14 route that smartly avoids the area with a strong multipath effect. To achieve this goal, the impact 15

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A New Path Planning Algorithm Based on GNSS Localization Error Map

Abstract: In 2012, he was a visiting scholar in University College London, U.K. His research interests include GNSS positioning in challenging environments and localization for autonomous driving vehicle and unmanned aerial vehicle.

Cited by 4 publications

References 31 publications

Adaptive covariance estimation of LiDAR‐based positioning errors for UAVs

Adaptive covariance estimation of LiDAR‐based positioning errors for UAVs

A novel GNSS based V2V cooperative localization to exclude multipath effect using consistency checks

A New Path Planning Algorithm Using a GNSS Localization Error Map for UAVs in an Urban Area

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