ADA-DPM: 基于神经描述符的自适应噪声过滤策略用于SLAM
ADA-DPM: A Neural Descriptors-based Adaptive Noise Filtering Strategy for SLAM
AI总结：
ADA-DPM通过动态分割、全局重要性评分和跨层图卷积模块，提升SLAM中动态物体干扰和噪声的鲁棒性。
摘要：
激光雷达SLAM在移动机器人导航和高精度地图构建中扮演着重要角色。然而，现有方法在动态物体比例高、点云失真和无结构环境的场景中往往面临定位精度与系统鲁棒性之间的权衡。为了解决这一问题，我们提出了一种基于神经描述符的自适应噪声过滤策略用于SLAM，命名为ADA-DPM，通过三个关键技术创新来提升定位和地图构建任务的性能。首先，为解决动态物体干扰，我们设计了动态分割头以预测并过滤出动态特征点，消除动态物体引起的自我运动干扰。其次，为减轻噪声和无结构特征点的影响，我们提出了全局重要性评分头，该头可以自适应地选择高贡献特征点并抑制噪声和无结构特征点的影响。此外，我们引入了跨层图卷积模块（GLI-GCN）以构建多尺度邻域图，融合不同尺度的局部结构信息，提高重叠特征的辨别能力。最后，多个公开数据集上的实验验证证实了ADA-DPM的有效性。
英文摘要：
Lidar SLAM plays a significant role in mobile robot navigation and high-definition map construction. However, existing methods often face a trade-off between localization accuracy and system robustness in scenarios with a high proportion of dynamic objects, point cloud distortion, and unstructured environments. To address this issue, we propose a neural descriptors-based adaptive noise filtering strategy for SLAM, named ADA-DPM, which improves the performance of localization and mapping tasks through three key technical innovations. Firstly, to tackle dynamic object interference, we design the Dynamic Segmentation Head to predict and filter out dynamic feature points, eliminating the ego-motion interference caused by dynamic objects. Secondly, to mitigate the impact of noise and unstructured feature points, we propose the Global Importance Scoring Head that adaptively selects high-contribution feature points while suppressing the influence of noise and unstructured feature points. Moreover, we introduce the Cross-Layer Graph Convolution Module (GLI-GCN) to construct multi-scale neighborhood graphs, fusing local structural information across different scales and improving the discriminative power of overlapping features. Finally, experimental validations on multiple public datasets confirm the effectiveness of ADA-DPM.
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