  In this paper, we present a novel visual SLAM and long-term localization
benchmark for autonomous driving in challenging conditions based on the
large-scale 4Seasons dataset. The proposed benchmark provides drastic
appearance variations caused by seasonal changes and diverse weather and
illumination conditions. While significant progress has been made in advancing
visual SLAM on small-scale datasets with similar conditions, there is still a
lack of unified benchmarks representative of real-world scenarios for
autonomous driving. We introduce a new unified benchmark for jointly evaluating
visual odometry, global place recognition, and map-based visual localization
performance which is crucial to successfully enable autonomous driving in any
condition. The data has been collected for more than one year, resulting in
more than 300 km of recordings in nine different environments ranging from a
multi-level parking garage to urban (including tunnels) to countryside and
highway. We provide globally consistent reference poses with up to
centimeter-level accuracy obtained from the fusion of direct stereo-inertial
odometry with RTK GNSS. We evaluate the performance of several state-of-the-art
visual odometry and visual localization baseline approaches on the benchmark
and analyze their properties. The experimental results provide new insights
into current approaches and show promising potential for future research. Our
benchmark and evaluation protocols will be available at
https://www.4seasons-dataset.com/.
