The real-world deployment of fully autonomous mobile robots depends on a robust simultaneous localization and mapping (SLAM) system, capable of handling dynamic environments, where objects are moving in front of the robot, and changing environments, where objects are moved or replaced after the robot has already mapped the scene. This paper proposes Changing-SLAM, a method for robust Visual SLAM in both dynamic and changing environments. This is achieved by using a Bayesian filter combined with a long-term data association algorithm. Also, it employs an efficient algorithm for dynamic keypoints filtering based on object detection that correctly identifies features inside the bounding box that are not dynamic, preventing a depletion of features that could cause lost tracks. Furthermore, a new dataset was developed with RGB-D data specially designed for the evaluation of changing environments on an object level, called PUC-USP dataset. Six sequences were created using a mobile robot, an RGB-D camera and a motion capture system. The sequences were designed to capture different scenarios that could lead to a tracking failure or map corruption. Changing-SLAM does not assume a given camera pose or a known map, being also able to operate in real time. The proposed method was evaluated using benchmark datasets and compared with other state-of-the-art methods, proving to be highly accurate.

完全自主移动机器人的实际部署取决于强大的同步定位和地图构建（SLAM）系统，该系统能够处理动态环境（物体在机器人前面移动）以及变化的环境（物体在机器人前面移动或替换）机器人已经绘制了场景地图。本文提出了 Changing-SLAM，一种在动态和变化的环境中实现鲁棒视觉 SLAM 的方法。这是通过使用贝叶斯过滤器与长期数据关联算法相结合来实现的。此外，它还采用基于对象检测的高效动态关键点过滤算法，可以正确识别边界框内非动态的特征，防止特征耗尽而导致轨迹丢失。此外，还使用专门设计用于评估对象级别不断变化的环境的 RGB-D 数据开发了一个新数据集，称为 PUC-USP 数据集。使用移动机器人、RGB-D 相机和动作捕捉系统创建了六个序列。这些序列旨在捕获可能导致跟踪失败或地图损坏的不同场景。 Change-SLAM 不假设给定的相机姿势或已知的地图，也能够实时操作。使用基准数据集对所提出的方法进行了评估，并与其他最先进的方法进行了比较，结果证明该方法非常准确。