对比g2o与Ceres在Cartographer中优化扫描匹配的性能表现
g2o vs. Ceres: Optimizing Scan Matching in Cartographer SLAM
- 用g2o和Ceres分别优化Cartographer中的扫描匹配
- Ceres更快收敛，更少迭代，地图更清晰
- g2o在局部障碍物检测上更优，适合特定场景
本文对g2o和Ceres求解器在Cartographer SLAM框架中提升扫描匹配性能的表现进行了对比分析。Cartographer是一个广泛使用的同步定位与地图构建（SLAM）库，依赖优化算法来精炼位姿估计并提高地图精度。研究旨在评估g2o与默认使用的Ceres求解器在性能、效率和准确性方面的差异。实验结果表明，在使用AgileX LIMO机器人的真实环境映射中，Ceres在速度、收敛效率和整体地图清晰度方面均优于g2o，所需迭代次数更少，耗时更短，生成的地图更准确且结构更分明。然而，g2o在局部障碍物检测方面表现更佳，凸显其在特定应用中的价值。
原文摘要 · Abstract (English)
This article presents a comparative analysis of g2o and Ceres solvers in enhancing scan matching performance within the Cartographer framework. Cartographer, a widely-used library for Simultaneous Localization and Mapping (SLAM), relies on optimization algorithms to refine pose estimates and improve map accuracy. The research aims to evaluate the performance, efficiency, and accuracy of the g2o solver in comparison to the Ceres solver, which is the default in Cartographer. In our experiments comparing Ceres and g2o within Cartographer, Ceres outperformed g2o in terms of speed, convergence efficiency, and overall map clarity. Ceres required fewer iterations and less time to converge, producing more accurate and well-defined maps, especially in real-world mapping scenarios with the AgileX LIMO robot. However, g2o excelled in localized obstacle detection, highlighting its value in specific situations.
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