Visually poor scenarios are one of the main sources of failure in visual
localization systems in outdoor environments. To address this challenge, we
present MOZARD, a multi-modal localization system for urban outdoor
environments using vision and LiDAR. By extending our preexisting key-point
based visual multi-session local localization approach with the use of semantic
data, an improved localization recall can be achieved across vastly different
appearance conditions. In particular we focus on the use of curbstone
information because of their broad distribution and reliability within urban
environments. We present thorough experimental evaluations on several driving
kilometers in challenging urban outdoor environments, analyze the recall and
accuracy of our localization system and demonstrate in a case study possible
failure cases of each subsystem. We demonstrate that MOZARD is able to bridge
scenarios where our previous work VIZARD fails, hence yielding an increased
recall performance, while a similar localization accuracy of 0.2m is achieve
