发表机构
School of Automation, Southeast University; Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich; Faculty of Data Science, City University of Macau（东南大学自动化学院; 慕尼黑工业大学机器人、人工智能与实时系统讲席; 澳门城市大学数据科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于重力先验的变换解耦与位姿优化算法，将6自由度绝对位姿估计简化为4自由度，经多数据集实验及集成ORB-SLAM2验证，性能优于现有SOTA方法，可减少轨迹漂移。
AI 中文摘要
物体绝对位姿估计是各类机器人应用中的核心任务。近年来，将重力方向作为先验信息已成为简化绝对位姿估计的热门方法，但由于存在大量误匹配，开发高效且鲁棒的算法解决该问题仍具挑战性；此外，利用带重力先验的选中内点对应关系获取精确位姿解仍是研究空白。本文提出一种利用重力先验推导的几何关系的新型变换策略，通过变换解耦，将原本6自由度（DoF）的绝对位姿估计问题简化为4自由度问题：1自由度用于旋转角，3自由度用于平移，显著提升了效率。针对1自由度旋转角，采用一维全局投票算法进行最优估计；获得最优旋转后，初步过滤误匹配对应关系，平移估计作为线性问题可轻松求解。此外，为获取精确位姿结果，引入新型位姿优化算法以提升旋转和平移的精度。在合成数据及三个公开真实数据集（TUM RGB-D、ETH3D和RobotCar）上的大量实验表明，所提方法相比现有最先进（SOTA）方法性能更强；为进一步验证，将其集成到ORB-SLAM2中，在KITTI数据集上的结果显示，该方法能有效减少漂移并提升重定位过程中的轨迹对齐度，源代码将在论文接收后发布。
英文摘要
Estimation of the absolute pose of an object is an essential task for various robotic applications. Recently, incorporating gravity direction as prior information has emerged as a popular approach to simplify absolute pose estimation. However, developing a robust and efficient algorithm to solve this challenging problem remains a difficult question due to large amounts of mismatches. In addition, obtaining an accurate pose solution from selected inlier correspondences with gravity prior is still a research gap. In this paper, we propose a novel transformation strategy that exploits geometric relations derived from the gravity prior. Through transformation decoupling, the original 6 degrees of freedom (DoF) absolute pose estimation problem is simplified into a 4-DoFs problem: 1-DoF for the rotation angle and 3-DoFs for translation, significantly improving the efficiency. For the 1-DoF rotation angle, we apply a one-dimensional global voting algorithm for optimal estimation. Once the optimal rotation is obtained, the mismatched correspondences are preliminarily filtered, and translation estimation, a linear problem, can be easily solved. Furthermore, to obtain accurate pose results, we introduce a novel pose refinement algorithm to enhance the accuracy of both rotation and translation. Extensive experiments on synthetic data and three publicly available real-world datasets (TUM RGB-D, ETH3D, and RobotCar) demonstrate that the proposed method achieves stronger performance compared to existing state-of-the-art (SOTA) approaches. To further validate our method, we integrated it into ORB-SLAM2. The results on the KITTI dataset show it effectively reduces drift and improves trajectory alignment during relocalization. The source code will be released upon acceptance.
CommentsThis work is accepted by IEEE Transactions on Image Processing
