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Visually poor scenarios are one of the main sources of failure in visuallocalization systems in outdoor environments. To address this challenge, wepresent MOZARD, a multi-modal localization system for urban outdoorenvironments using vision and LiDAR. By extending our preexisting key-pointbased visual multi-session local localization approach with the use of semanticdata, an improved localization recall can be achieved across vastly differentappearance conditions. In particular we focus on the use of curbstoneinformation because of their broad distribution and reliability within urbanenvironments. We present thorough experimental evaluations on several drivingkilometers in challenging urban outdoor environments, analyze the recall andaccuracy of our localization system and demonstrate in a case study possiblefailure cases of each subsystem. We demonstrate that MOZARD is able to bridgescenarios where our previous work VIZARD fails, hence yielding an increasedrecall performance, while a similar localization accuracy of 0.2m is achieved

MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments