对比64与128通道激光雷达对图优化SLAM的影响，揭示分辨率在城市环境中的关键作用。
Impact of 3D LiDAR Resolution in Graph-based SLAM Approaches: A Comparative Study
- 基于图优化框架，比较四种3D LiDAR SLAM方法在不同分辨率下的表现。
- 在KITTI和柏林新数据集上验证，128通道雷达显著提升定位精度与地图完整性。
- 为自动驾驶系统选型提供实证依据，适合关注高精地图构建的工程团队。
同时定位与建图（SLAM）是自主系统在需持续地图支持环境中实现可靠定位的关键组件。尽管相机与激光雷达是主流方案，早期方法多依赖二维数据，近年则转向三维。本文针对城市环境中基于3D激光雷达的图优化SLAM方法进行综述，重点比较其优缺点与局限性。进一步评估了64通道与128通道激光雷达在鲁棒性上的差异。实验在真实城市场景中开展，使用KITTI里程计数据集（仅64通道激光雷达）和全新采集的AUTONOMOS-LABS数据集（含64与128通道激光雷达）。结果以定量指标呈现，并辅以定性地图分析。
原文摘要 · Abstract (English)
Simultaneous Localization and Mapping (SLAM) is a key component of autonomous systems operating in environments that require a consistent map for reliable localization. SLAM has been a widely studied topic for decades with most of the solutions being camera or LiDAR based. Early LiDAR-based approaches primarily relied on 2D data, whereas more recent frameworks use 3D data. In this work, we survey recent 3D LiDAR-based Graph-SLAM methods in urban environments, aiming to compare their strengths, weaknesses, and limitations. Additionally, we evaluate their robustness regarding the LiDAR resolution namely 64 $vs$ 128 channels. Regarding SLAM methods, we evaluate SC-LeGO-LOAM, SC-LIO-SAM, Cartographer, and HDL-Graph on real-world urban environments using the KITTI odometry dataset (a LiDAR with 64-channels only) and a new dataset (AUTONOMOS-LABS). The latter dataset, collected using instrumented vehicles driving in Berlin suburban area, comprises both 64 and 128 LiDARs. The experimental results are reported in terms of quantitative `metrics' and complemented by qualitative maps.
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