CARLA-Loc: Synthetic SLAM Dataset with Full-stack Sensor Setup in Challenging Weather and Dynamic Environments
arxiv(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要