EqNIO：亚等变神经惯性里程计
EqNIO: Subequivariant Neural Inertial Odometry
AI总结：
针对神经惯性里程计未考虑IMU数据物理旋转-反射对称性导致泛化受限的问题，提出EqNIO框架，通过三步等变处理设计网络，在多个数据集上表现优于现有方法。
摘要：
神经网络正在纯惯性里程计中得到迅速采用，其中来自商用惯性测量单元（IMU）的加速度计和陀螺仪测量值被用于回归位移及相关的不确定性。它们能够学习信息丰富的位移先验，这些先验可以直接与原始数据通过现成的非线性滤波器进行融合。然而，这些网络没有考虑IMU数据中固有的物理旋转-反射对称性，导致需要为每个可能的运动方向记忆相同的先验，这阻碍了泛化能力。在这项工作中，我们表征了这些对称性，并证明了当围绕重力向量旋转以及相对于平行于重力的任意平面反射时，IMU数据以及由此产生的位移和协方差会进行等变变换。我们设计了一个神经网络，通过三步等变处理在设计上尊重这些对称性：首先，它利用专为与底层对称变换交换而定制的富有表现力的线性和非线性层，从源自IMU数据的等变向量和不变标量中估计一个等变的重力对齐坐标系。然后，我们将IMU数据映射到该坐标系中，从而实现一种不变规范化，可以直接与现成的惯性里程计网络一起使用。最后，我们将这些网络输出映射回原始坐标系，从而获得等变的协方差和位移。我们通过将我们的框架应用于基于TLIO的滤波方法和端到端RONIN架构，证明了我们框架的通用性，并展示了在TLIO、Aria、RIDI和OxIOD数据集上比现有方法更好的性能。
英文摘要：
Neural networks are seeing rapid adoption in purely inertial odometry, where accelerometer and gyroscope measurements from commodity inertial measurement units (IMU) are used to regress displacements and associated uncertainties. They can learn informative displacement priors, which can be directly fused with the raw data with off-the-shelf non-linear filters. Nevertheless, these networks do not consider the physical roto-reflective symmetries inherent in IMU data, leading to the need to memorize the same priors for every possible motion direction, which hinders generalization. In this work, we characterize these symmetries and show that the IMU data and the resulting displacement and covariance transform equivariantly, when rotated around the gravity vector and reflected with respect to arbitrary planes parallel to gravity. We design a neural network that respects these symmetries by design through equivariant processing in three steps: First, it estimates an equivariant gravity-aligned frame from equivariant vectors and invariant scalars derived from IMU data, leveraging expressive linear and non-linear layers tailored to commute with the underlying symmetry transformation. We then map the IMU data into this frame, thereby achieving an invariant canonicalization that can be directly used with off-the-shelf inertial odometry networks. Finally, we map these network outputs back into the original frame, thereby obtaining equivariant covariances and displacements. We demonstrate the generality of our framework by applying it to the filter-based approach based on TLIO, and the end-to-end RONIN architecture, and show better performance on the TLIO, Aria, RIDI and OxIOD datasets than existing methods.
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