ALIVE-LIO: Degeneracy-Aware Learning of Inertial Velocity for Enhancing ESKF-Based LiDAR-Inertial Odometry

arXiv cs.RO / 4/6/2026

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Key Points

The paper introduces ALIVE-LIO, a degeneracy-aware LiDAR-inertial odometry framework designed to improve state estimation in environments with poor LiDAR observability (e.g., long corridors or single-wall, narrow FOV scenes).
ALIVE-LIO integrates a deep neural network into a classical error-state Kalman filter (ESKF) by predicting body-frame velocity and fusing it into the filter only when degeneracy is detected.
This selective fusion aims to preserve the probabilistic consistency and structure of the ESKF while compensating for the directions where LiDAR measurements provide limited information.
Experiments on public datasets with degeneracy and on the authors’ own collected data show substantially reduced pose drift, achieving top/competitive performance in 22 of 32 sequences.
The authors state that the ALIVE-LIO implementation will be publicly released, supporting reproducibility and adoption in LiDAR-inertial odometry research and applications.

Abstract

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 most competitive results in 22 out of 32 sequences. The implementation of ALIVE-LIO will be publicly available.