$\mathbf{SO}(3)$ 上带偏差补偿的标量测量姿态估计
Scalar-Measurement Attitude Estimation on $\mathbf{SO}(3)$ with Bias Compensation
AI总结：
本文提出基于标量测量的 $\mathbf{SO}(3)$ 非线性确定性观测器，结合陀螺仪偏差补偿，在适当可观测性条件下实现局部指数稳定，并证明两个标量测量在合适激励下足以进行姿态估计，三个在静态情况下足够。
摘要：
姿态估计方法通常依赖于来自惯性传感器（如加速度计和磁力计）的完整矢量测量。本文表明，仅使用标量测量也能实现可靠估计，这些标量测量自然出现为矢量读数的分量或来自其他传感模态的独立约束。我们提出了 $\mathbf{SO}(3)$ 上的非线性确定性观测器，该观测器结合了陀螺仪偏差补偿，并在适当的可观测性条件下保证均匀局部指数稳定性。该框架的一个关键特性是对部分感知的鲁棒性：即使只有矢量分量的子集可用，也能保持准确估计。在 BROAD 数据集上的实验验证确认了在逐步减少的测量配置下性能一致，即使在严重信息丢失的情况下估计误差仍然很小。据我们所知，这是第一项建立基本可观测性结果的工作，表明在适当激励下两个标量测量足以进行姿态估计，而在静态情况下三个足够。这些结果将基于标量测量的观测器定位为传统基于矢量方法的实用且可靠的替代方案。
英文摘要：
Attitude estimation methods typically rely on full vector measurements from inertial sensors such as accelerometers and magnetometers. This paper shows that reliable estimation can also be achieved using only scalar measurements, which naturally arise either as components of vector readings or as independent constraints from other sensing modalities. We propose nonlinear deterministic observers on $\mathbf{SO}(3)$ that incorporate gyroscope bias compensation and guarantee uniform local exponential stability under suitable observability conditions. A key feature of the framework is its robustness to partial sensing: accurate estimation is maintained even when only a subset of vector components is available. Experimental validation on the BROAD dataset confirms consistent performance across progressively reduced measurement configurations, with estimation errors remaining small even under severe information loss. To the best of our knowledge, this is the first work to establish fundamental observability results showing that two scalar measurements under suitable excitation suffice for attitude estimation, and that three are enough in the static case. These results position scalar-measurement-based observers as a practical and reliable alternative to conventional vector-based approaches.
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。
