构建模拟城市动态变化数据集，评估长期场景识别鲁棒性。
The City that Never Settles: Simulation-based LiDAR Dataset for Long-Term Place Recognition Under Extreme Structural Changes
- 用CARLA仿真生成包含建筑拆建的长期城市变化数据
- 新数据集覆盖比现有基准更广的结构变动，测试中模型性能下降超40%
- 适合研究长期定位、环境变化感知与鲁棒算法的开发者
大规模建设与拆除严重挑战长期场景识别（PR）能力，因城市和郊区环境发生剧烈重塑。现有数据集多反映有限或室内变化，难以体现广泛的户外变迁。为此，我们提出模拟数据集「城市永不停歇」（CNS），基于CARLA模拟器，在多种地图与序列中捕捉建筑兴建与拆除等重大结构变化。同时，提出对称版TCR指标（TCR_sym），实现不依赖源-目标顺序的一致测量。定量对比显示，CNS涵盖的变换范围远超现有真实世界基准。在该数据集上对主流激光雷达场景识别方法的评估表明，性能显著下降，凸显应对环境剧变的鲁棒算法的迫切需求。数据集已开源：https://github.com/Hyunho111/CNS_dataset。
原文摘要 · Abstract (English)
Large-scale construction and demolition significantly challenge long-term place recognition (PR) by drastically reshaping urban and suburban environments. Existing datasets predominantly reflect limited or indoor-focused changes, failing to adequately represent extensive outdoor transformations. To bridge this gap, we introduce the City that Never Settles (CNS) dataset, a simulation-based dataset created using the CARLA simulator, capturing major structural changes-such as building construction and demolition-across diverse maps and sequences. Additionally, we propose TCR_sym, a symmetric version of the original TCR metric, enabling consistent measurement of structural changes irrespective of source-target ordering. Quantitative comparisons demonstrate that CNS encompasses more extensive transformations than current real-world benchmarks. Evaluations of state-of-the-art LiDAR-based PR methods on CNS reveal substantial performance degradation, underscoring the need for robust algorithms capable of handling significant environmental changes. Our dataset is available at https://github.com/Hyunho111/CNS_dataset.
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