  The robustness of SLAM (Simultaneous Localization and Mapping) algorithms
under challenging environmental conditions is critical for the success of
autonomous driving. However, the real-world impact of such conditions remains
largely unexplored due to the difficulty of altering environmental parameters
in a controlled manner. To address this, we introduce CARLA-Loc, a synthetic
dataset designed for challenging and dynamic environments, created using the
CARLA simulator. Our dataset integrates a variety of sensors, including
cameras, event cameras, LiDAR, radar, and IMU, etc. with tuned parameters and
modifications to ensure the realism of the generated data. CARLA-Loc comprises
7 maps and 42 sequences, each varying in dynamics and weather conditions.
Additionally, a pipeline script is provided that allows users to generate
custom sequences conveniently. We evaluated 5 visual-based and 4 LiDAR-based
SLAM algorithms across different sequences, analyzing how various challenging
environmental factors influence localization accuracy. Our findings demonstrate
the utility of the CARLA-Loc dataset in validating the efficacy of SLAM
algorithms under diverse conditions.
