arXiv 2402.136092024-02-26cs.ROcs.CV

VOOM：使用分层地标的鲁棒视觉物体里程计与建图

VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks

School of Mechanical Engineering, Beijing Institute of Technology（北京理工大学机械工程学院）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）

Yutong Wang, Chaoyang Jiang, Xieyuanli Chen

备注：: 7 pages, 5 figures, 4 tables, conference icra 2024 accepted

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出VOOM框架，以从粗到细方式融合高层物体与低层特征点作为分层地标，改进对偶二次曲面观测模型与数据关联，并利用物体信息增强特征点关联和局部光束法平差，在定位精度上优于ORB-SLAM2等系统。

摘要：

近年来，面向物体的同步定位与建图（SLAM）由于能够在保持计算效率的同时提供高层语义信息而受到越来越多的关注。一些研究者尝试通过将建模的物体残差集成到光束法平差中来提升定位精度。然而，很少有方法能展现出比基于特征的视觉SLAM系统更好的结果，因为通用的粗略物体模型（如长方体或椭球体）不如特征点精确。在本文中，我们提出了一种视觉物体里程计与建图框架VOOM，该框架以从粗到细的方式使用高层物体和低层点作为分层地标，而不是在光束法平差中直接使用物体残差。首先，我们为对偶二次曲面引入了一种改进的观测模型和一种新颖的数据关联方法，用于表示物理物体。这有助于创建更贴近现实的三维地图。其次，我们利用物体信息增强特征点的数据关联，并据此更新地图。在视觉物体里程计后端中，更新后的地图被用于进一步优化相机位姿和物体。同时，在我们的视觉物体建图过程中，利用基于物体和点的共视图执行局部光束法平差。实验表明，VOOM在定位方面优于面向物体的SLAM和基于特征点的SLAM系统（如ORB-SLAM2）。我们的方法实现可在https://github.com/yutongwangBIT/VOOM.git获取。

英文摘要：

In recent years, object-oriented simultaneous localization and mapping (SLAM) has attracted increasing attention due to its ability to provide high-level semantic information while maintaining computational efficiency. Some researchers have attempted to enhance localization accuracy by integrating the modeled object residuals into bundle adjustment. However, few have demonstrated better results than feature-based visual SLAM systems, as the generic coarse object models, such as cuboids or ellipsoids, are less accurate than feature points. In this paper, we propose a Visual Object Odometry and Mapping framework VOOM using high-level objects and low-level points as the hierarchical landmarks in a coarse-to-fine manner instead of directly using object residuals in bundle adjustment. Firstly, we introduce an improved observation model and a novel data association method for dual quadrics, employed to represent physical objects. It facilitates the creation of a 3D map that closely reflects reality. Next, we use object information to enhance the data association of feature points and consequently update the map. In the visual object odometry backend, the updated map is employed to further optimize the camera pose and the objects. Meanwhile, local bundle adjustment is performed utilizing the objects and points-based covisibility graphs in our visual object mapping process. Experiments show that VOOM outperforms both object-oriented SLAM and feature points SLAM systems such as ORB-SLAM2 in terms of localization. The implementation of our method is available at https://github.com/yutongwangBIT/VOOM.git.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。