ALIVE-LIO: 退化感知的惯性速度学习以增强基于误差状态卡尔曼滤波的LiDAR-惯性里程计
ALIVE-LIO: Degeneracy-Aware Learning of Inertial Velocity for Enhancing ESKF-Based LiDAR-Inertial Odometry
AI总结：
ALIVE-LIO通过将深度神经网络整合到经典误差状态卡尔曼滤波中，提升退化环境中LiDAR可观测性，减少姿态漂移，实验显示在22/32序列中表现最佳。
摘要：
利用LiDAR和惯性测量单元（IMU）的里程计（LIO）在退化环境中性能下降，如长走廊或单墙场景。本文提出ALIVE-LIO框架，通过深度神经网络补偿LiDAR可观测性损失。ALIVE-LIO预测体坐标系速度并在检测到退化时选择性融合至ESKF，提供有效状态更新。实验表明，ALIVE-LIO在退化环境中显著减少姿态漂移，在22/32序列中表现最佳。
英文摘要：
Odometry estimation using light detection and ranging (LiDAR) and an inertial measurement unit (IMU), known as LiDAR-inertial odometry (LIO), often suffers from performance degradation in degenerate environments, such as long corridors or single-wall scenarios with narrow field-of-view LiDAR. To address this limitation, we propose ALIVE-LIO, a degeneracy-aware LiDAR-inertial odometry framework that explicitly enhances state estimation in degenerate directions. The key contribution of ALIVE-LIO is the strategic integration of a deep neural network into a classical error-state Kalman filter (ESKF) to compensate for the loss of LiDAR observability. Specifically, ALIVE-LIO employs a neural network to predict the body-frame velocity and selectively fuses this prediction into the ESKF only when degeneracy is detected, providing effective state updates along degenerate directions. This design enables ALIVE-LIO to utilize the probabilistic structure and consistency of the ESKF while benefiting from learning-based motion estimation. The proposed method was evaluated on publicly available datasets exhibiting degeneracy, as well as on our own collected data. Experimental results demonstrate that ALIVE-LIO substantially reduces pose drift in degenerate environments, yielding the top-ranked result in 22 out of 32 sequences. Our code is available at https://github.com/cocel-postech/alive-lio.
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。
