动态环境下通过物体与场景联合评估，减少定位漂移。
Adaptive Prior Scene-Object SLAM for Dynamic Environments
- 基于场景与物体的可靠性评估框架，实时判断帧质量与变化。
- 在TUM RGB-D数据集上定位误差显著降低，鲁棒性提升。
- 适合自动驾驶、机器人导航等动态环境定位需求。
视觉同步定位与地图构建（SLAM）在自主系统实时定位中至关重要。然而，传统SLAM方法假设环境静态，在动态场景中常出现严重定位漂移。尽管近期进展提升了动态环境下的性能，但系统仍受视角突变和运动物体表征不足的影响，导致定位漂移。本文提出一种基于场景-物体的可靠性评估框架，综合当前帧质量与相对于可靠参考帧的场景变化来评估SLAM稳定性。为解决现有系统在位姿估计不可靠时缺乏误差修正的问题，我们采用一种位姿优化策略，利用可靠帧信息改进相机位姿估计，有效缓解动态干扰。在TUM RGB-D数据集上的大量实验表明，该方法在复杂动态场景中显著提升了定位精度与系统鲁棒性。
原文摘要 · Abstract (English)
Visual Simultaneous Localization and Mapping (SLAM) plays a vital role in real-time localization for autonomous systems. However, traditional SLAM methods, which assume a static environment, often suffer from significant localization drift in dynamic scenarios. While recent advancements have improved SLAM performance in such environments, these systems still struggle with localization drift, particularly due to abrupt viewpoint changes and poorly characterized moving objects. In this paper, we propose a novel scene-object-based reliability assessment framework that comprehensively evaluates SLAM stability through both current frame quality metrics and scene changes relative to reliable reference frames. Furthermore, to tackle the lack of error correction mechanisms in existing systems when pose estimation becomes unreliable, we employ a pose refinement strategy that leverages information from reliable frames to optimize camera pose estimation, effectively mitigating the adverse effects of dynamic interference. Extensive experiments on the TUM RGB-D datasets demonstrate that our approach achieves substantial improvements in localization accuracy and system robustness under challenging dynamic scenarios.
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