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In this paper, we present a novel visual SLAM and long-term localizationbenchmark for autonomous driving in challenging conditions based on thelarge-scale 4Seasons dataset. The proposed benchmark provides drasticappearance variations caused by seasonal changes and diverse weather andillumination conditions. While significant progress has been made in advancingvisual SLAM on small-scale datasets with similar conditions, there is still alack of unified benchmarks representative of real-world scenarios forautonomous driving. We introduce a new unified benchmark for jointly evaluatingvisual odometry, global place recognition, and map-based visual localizationperformance which is crucial to successfully enable autonomous driving in anycondition. The data has been collected for more than one year, resulting inmore than 300 km of recordings in nine different environments ranging from amulti-level parking garage to urban (including tunnels) to countryside andhighway. We provide globally consistent reference poses with up tocentimeter-level accuracy obtained from the fusion of direct stereo-inertialodometry with RTK GNSS. We evaluate the performance of several state-of-the-artvisual odometry and visual localization baseline approaches on the benchmarkand analyze their properties. The experimental results provide new insightsinto current approaches and show promising potential for future research. Ourbenchmark and evaluation protocols will be available athttps://www.4seasons-dataset.com/.

4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions