VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments

Mathias Buerki, Lukas Schaupp, Marcyn Dymczyk, Renaud Dube, Cesar Cadena, Roland Siegwart, and Juan Nieto

IEEE Intelligent Vehicles Symposium (IV) 2019

Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multi-session maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheel-odometry in a state estimation framework further guarantees smooth and accurate pose estimates. In an extensive experimental evaluation on several hundreds of driving kilometers in challenging urban outdoor environments, we analyze the recall and accuracy of our localization system, investigate its key parameters and boundary conditions, and compare different types of feature descriptors. Our results show that VIZARD is able to achieve nearly 100% recall with a localization accuracy below 0.5m under varying outdoor appearance conditions, including at night-time.

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Title = {Map Management for Efficient Long-Term Visual Localization in Outdoor Environments},
Author = {M. Buerki and L. Schaupp and M. Dymczyk and R. Dube and C. Cadena and R. Siegwart and J. Nieto},
Fullauthor = {Mathias Buerki and Lukas Schaupp and Marcyn Dymczyk and Renaud Dube and Cesar Cadena and Roland Siegwart and Juan Nieto},
Booktitle = {{IEEE} Intelligent Vehicles Symposium ({IV})},
Month = {June},
Year = {2019},