Robust Vehicle Localization in Urban Environments Using ...

Robust Vehicle Localization in UrbanEnvironments Using probabilistic MapsJesse Levinson, Sebastian ThrunStanford Artificial Intelligence Autonomous Vehicle navigation in dynamic urbanenvironments requires Localization accuracy exceeding thatavailable from GPS-based inertial guidance systems. We haveshown previously that GPS, IMU, and LIDAR data can beused to generate a high-resolution infrared remittance groundmap that can be subsequently used for Localization [4]. Wenow propose an extension to this approach that yields substan-tial improvements over previous work in Vehicle Localization ,including higher precision, the ability to learn and improvemaps over time, and increased robustness to environmentchanges and dynamic obstacles. Specifically, we model theenvironment, instead of as a spatial grid of fixed infraredremittance values, as a probabilistic grid whereby every cellis represented as its own gaussian distribution over remittancevalues.

Robust Vehicle Localization in Urban Environments Using Probabilistic Maps Jesse Levinson, Sebastian Thrun Stanford Artificial Intelligence Laboratory ... localization system should be able to handle situations where the environment has changed since the map was created.

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  Using, Vehicle, Environment, Urban, Amps, Probabilistic, Localization, Vehicle localization in urban environments, Vehicle localization in urban environments using probabilistic maps

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