Simultaneous localization and mapping (SLAM) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these algorithms in real-world environments. In this paper, we provide an evaluation strategy and real-world datasets to test and evaluate SLAM algorithms in complex and challenging indoor environments. Further, we analysed state-of-the-art (SOTA) SLAM algorithms based on various metrics such as absolute trajectory error, scale drift, and map accuracy and consistency. Our results demonstrate that SOTA SLAM algorithms often fail in challenging environments, with dynamic objects, transparent and reflecting surfaces. We also found that successful loop closures had a significant impact on the algorithm's performance. These findings highlight the need for further research to improve the robustness of the algorithms in real-world scenarios.

翻译：同时定位与地图构建（SLAM）算法对于移动机器人的自主导航至关重要。随着对自主系统需求的日益增长，在真实环境中评估和比较这些算法的性能变得至关重要。本文提出了一种评估策略，并提供了真实世界的数据集，用于在复杂且具有挑战性的室内环境中测试和评估SLAM算法。此外，我们基于绝对轨迹误差、尺度漂移、地图精度与一致性等多种指标，分析了当前最先进的SLAM算法。结果表明，在存在动态物体、透明表面以及反射表面的挑战性环境中，最先进的SLAM算法常常失败。我们还发现，成功的闭环检测对算法的性能有显著影响。这些发现凸显了进一步提升算法在真实场景中鲁棒性的必要性。