AI 中文总结
针对地面约束机器人的传感器校准难题，提出PLS-Calib框架，利用PLS回归建模传感器流相关性，结合极性感知事件表示，在合成与真实数据集上验证其校准鲁棒性和精度优于现有最优方法。
AI 中文摘要
传感器之间准确的外参旋转校准是机器人感知系统性能的基础。然而，大多数现有的校准技术依赖完全6自由度运动来激发所有自由度，这对于运动能力有限的受地面约束机器人来说通常是不可行的。针对这类受限场景的现有方法，例如基于典型相关分析（CCA）的方法，存在协方差矩阵病态的问题，导致数值不稳定和校准精度欠佳。为克服这些局限性，我们提出了一种名为PLS-Calib的新型旋转校准框架，该框架首次利用偏最小二乘（PLS）回归对异步异构传感器流之间的潜在运动学相关性进行建模。具体而言，我们将该方法应用于地面机器人上的事件相机与里程计的校准。为改进基于事件的模式检测，我们引入了一种极性感知事件表示，可增强圆形校准靶标的时空对比度。我们基于PLS的公式得到了闭式稳定解，避免了基于CCA的方法中固有的矩阵奇异性。在合成数据集和真实世界数据集上的大量实验验证了我们方法的有效性，表明其在校准鲁棒性和精度上相较于现有最优方法有显著提升。本工作为受限机器人系统中的旋转校准提供了一种实用且有理论依据的解决方案，并为将统计学习技术应用于神经形态视觉开辟了新方向。
英文摘要
Accurate extrinsic rotation calibration between sensors is fundamental to the performance of robotic perception systems. However, most existing calibration techniques rely on full 6-DoF motion to excite all degrees of freedom, which is often infeasible for ground-constrained robots with limited motion capabilities. Recent approaches designed for such restricted settings, such as Canonical Correlation Analysis (CCA)-based methods, suffer from ill-conditioned covariance matrices that lead to numerical instability and suboptimal calibration accuracy. To overcome these limitations, we present a novel rotation calibration framework named PLS-Calib that, for the first time, leverages Partial Least Squares (PLS) regression to model the latent kinematic correlations between asynchronous, heterogeneous sensor streams. Specifically, we apply our method to the calibration of an event camera and an odometry onboard a ground robot. To improve event-based pattern detection, we introduce a polarity-aware event representation, which enhances spatiotemporal contrast in circular calibration targets. Our PLS-based formulation yields a closed-form, stable solution that avoids matrix singularities inherent in CCA-based approaches. Extensive experiments on both synthetic and real-world datasets validate the effectiveness of our approach, demonstrating significant improvements in calibration robustness and accuracy over state-of-the-art methods. This work offers a practical and theoretically grounded solution for rotation calibration in constrained robotic systems and opens up new directions for applying statistical learning techniques in neuromorphic vision.
Comments8 pages, 10 figures, 4 tables. Accepted at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
