000			01675cam a2200157 4500
100	1		_aGUPTA Shubh
700			_aGAO Grace
245			_aData-driven protection levels for camera and 3D map-based safe urban localization/ _cShubh Gupta and Grace Gao
260			_c2021
520			_aReliably 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.
650			_aDEEP LEARNING _xIMAGE REGISTRATION _xINTEGRITY MONITORING _xLOCALIZATION SAFETY _xPROTECTION LEVEL _xVISION-BASED LOCALIZATION
773			_aNavigation : _gVol 68 No 3, Fall 2021, pp.643-660 (43)
598			_aIT, TECHNOLOGY
856			_uhttps://www.ion.org/publications/abstract.cfm?articleID=102928 _zClick here for more
945			_i66856.1001 _rY _sY
999			_c40974 _d40974