High-definition mapping is fundamental for self-driving vehicles. In this thesis we describe different approaches for online map validation whose goal is to verify if reality and map data are inconsistent. A probabilistic framework to perform the sensor fusion is defined and a spatial correlation is introduced to interpolate the information. The result is a probabilistic representation of the map whose assumed values represent the probability with which the map is valid in every point.
Map Validation for Autonomous Driving Systems
Fabris, Andrea
2020/2021
Abstract
High-definition mapping is fundamental for self-driving vehicles. In this thesis we describe different approaches for online map validation whose goal is to verify if reality and map data are inconsistent. A probabilistic framework to perform the sensor fusion is defined and a spatial correlation is introduced to interpolate the information. The result is a probabilistic representation of the map whose assumed values represent the probability with which the map is valid in every point.File in questo prodotto:
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Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/20.500.12608/22888