Data‐driven protection levels for camera and 3D map‐based safe urban localization

Gupta, Shubh; Gao, Grace Xingxin

doi:10.1002/navi.445

Cited by 4 publications

(1 citation statement)

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“…In this paper, we propose a novel method for computing protection levels associated with a given vehicular state estimate (position and orientation) from camera image measurements and a 3D map of the environment. This work is based on our recent ION GNSS+ 2020 conference paper [28] and includes additional experiments and improvements to the DNN training process. Recently, high-definition 3D environment maps in the form of LiDAR point clouds have become increasingly available through industry players such as HERE, TomTom, Waymo and NVIDIA, as well as through projects such as USGS 3DEP [29] and OpenTopography [30].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Protection Levels for Camera and 3D Map-based Safe Urban Localization

Gupta¹,

Gao²

2021

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Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle's safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on the position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose a novel approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we compute the PLs by evaluating the position error bound using numerical line-search methods. Through experimental validation with real-world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments.Recently, data-driven techniques based on deep neural networks (DNNs) have demonstrated state-of-the-art performance in determining the state of the camera sensor, comprising of its position and orientation, by identifying and matching

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Section: Introductionmentioning

confidence: 99%