arXiv 2505.093562025-05-14cs.ROcs.CV

APR-Transformer：通过绝对位姿回归实现复杂环境中定位的初始位姿估计

APR-Transformer: Initial Pose Estimation for Localization in Complex Environments through Absolute Pose Regression

Faculty of Electrical Engineering and Computer Science, Chair of Agent Technology, Technische Universität Berlin（电气工程与计算机科学学院，智能体技术教授，柏林技术大学）；Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学学院，柏林技术大学）

Srinivas Ravuri, Yuan Xu, Martin Ludwig Zehetner, Ketan Motlag, Sahin Albayrak

备注：: 8 pages with 6 figures

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AI总结：

提出APR-Transformer，利用图像或LiDAR进行绝对位姿回归，为复杂及GNSS拒止环境下的定位提供精确初始位姿，并在多个数据集和实车实时部署中验证其先进性能。

摘要：

精确初始化对定位算法的性能起着至关重要的作用，尤其是在机器人、自动驾驶和计算机视觉领域。定位精度不佳往往源于初始位姿不准确，这一问题在主要依赖GPS信号进行初始化的GNSS拒止环境中尤为明显。近年来，利用深度神经网络进行位姿回归的进展显著提升了精度和鲁棒性，尤其是在估计复杂空间关系和朝向方面。本文提出APR-Transformer，这是一种受最先进方法启发的模型架构，可利用图像或LiDAR数据预测绝对位姿（三维位置和三维朝向）。我们证明，所提方法在Radar Oxford Robot-Car和DeepLoc等公认基准数据集上达到了最先进性能。此外，我们将实验扩展到自建的复杂APR-BeIntelli数据集。我们还通过在自动驾驶测试车辆上实时部署该模型，验证了其在GNSS拒止环境中的可靠性。这展示了该方法的实际可行性和有效性。源代码可在https://github.com/GT-ARC/APR-Transformer获取。

英文摘要：

Precise initialization plays a critical role in the performance of localization algorithms, especially in the context of robotics, autonomous driving, and computer vision. Poor localization accuracy is often a consequence of inaccurate initial poses, particularly noticeable in GNSS-denied environments where GPS signals are primarily relied upon for initialization. Recent advances in leveraging deep neural networks for pose regression have led to significant improvements in both accuracy and robustness, especially in estimating complex spatial relationships and orientations. In this paper, we introduce APR-Transformer, a model architecture inspired by state-of-the-art methods, which predicts absolute pose (3D position and 3D orientation) using either image or LiDAR data. We demonstrate that our proposed method achieves state-of-the-art performance on established benchmark datasets such as the Radar Oxford Robot-Car and DeepLoc datasets. Furthermore, we extend our experiments to include our custom complex APR-BeIntelli dataset. Additionally, we validate the reliability of our approach in GNSS-denied environments by deploying the model in real-time on an autonomous test vehicle. This showcases the practical feasibility and effectiveness of our approach. The source code is available at:https://github.com/GT-ARC/APR-Transformer.

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。