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.

翻译：SLAM（同步定位与地图构建）算法在严苛环境条件下的鲁棒性对自动驾驶的成功至关重要。然而，由于难以以受控方式改变环境参数，此类条件对导航性能的真实影响尚未得到充分探索。为解决这一问题，我们提出了CARLA-Loc——一个基于CARLA仿真器构建的面向挑战性与动态环境的合成数据集。该数据集集成了多种传感器，包括相机、事件相机、激光雷达、雷达及惯性测量单元等，并通过参数调优与功能改造确保生成数据的逼真度。CARLA-Loc包含7个地图与42个序列，每个序列在动态程度和天气条件上均有差异。此外，我们提供了流水线脚本，用户可便捷地生成自定义序列。我们基于不同序列对5种视觉SLAM算法和4种激光雷达SLAM算法进行评估，分析了多种挑战性环境因素对定位精度的影响。研究结果表明，CARLA-Loc数据集可有效验证SLAM算法在多样化条件下的性能。