用事件触发方式提升惯性里程计的泛化能力
Neural Inertial Odometry from Lie Events
- 用李代数上的事件信号替代原始惯性数据，避免采样率影响
- 在不同轨迹下使下游方法误差降低21%，仅需少量预处理
- 为多类传感器应用事件式采样提供新思路，适合做系统优化
神经位移先验（NDP）可减少惯性里程计的漂移并提供不确定性估计，便于与现有滤波器融合。但其对不同IMU采样率和轨迹模式缺乏泛化能力，限制了实际鲁棒性。为此，本文将传统输入的原始惯性数据替换为对李群SE(3)代数中预积分变化范数超过阈值时生成的李事件。受事件视觉启发，将一维信号的过零检测推广至李代数上的水平穿越，并引入归一化的李极性。实验表明，在李事件上训练的NDP能将下游惯性里程计方法的轨迹误差降低最多21%，且仅需极少预处理。作者推测，众多传感器可从事件式采样中获益，本工作为此方向迈出关键一步。
原文摘要 · Abstract (English)
Neural displacement priors (NDP) can reduce the drift in inertial odometry and provide uncertainty estimates that can be readily fused with off-the-shelf filters. However, they fail to generalize to different IMU sampling rates and trajectory profiles, which limits their robustness in diverse settings. To address this challenge, we replace the traditional NDP inputs comprising raw IMU data with Lie events that are robust to input rate changes and have favorable invariances when observed under different trajectory profiles. Unlike raw IMU data sampled at fixed rates, Lie events are sampled whenever the norm of the IMU pre-integration change, mapped to the Lie algebra of the SE(3) group, exceeds a threshold. Inspired by event-based vision, we generalize the notion of level-crossing on 1D signals to level-crossings on the Lie algebra and generalize binary polarities to normalized Lie polarities within this algebra. We show that training NDPs on Lie events incorporating these polarities reduces the trajectory error of off-the-shelf downstream inertial odometry methods by up to 21% with only minimal preprocessing. We conjecture that many more sensors than IMUs or cameras can benefit from an event-based sampling paradigm and that this work makes an important first step in this direction.
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