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

Autor:	Shubh Gupta, Grace Xingxin Gao
Rok vydání:	2021
Předmět:	GNSS applications Computer science Position (vector) Outlier Probabilistic logic Aerospace Engineering Image registration Probability distribution Electrical and Electronic Engineering Mixture model Algorithm Weighting
Zdroj:	NAVIGATION. 68:643-660
ISSN:	2161-4296 0028-1522
Popis:	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 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 an 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 PL 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5b46b805792a17569d12b3449534dc52 https://doi.org/10.1002/navi.445 Zobrazit plný text záznamu Plný text