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

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.

Figures (8)

• Figure 1: Comparison of state update methods. The state $\mathbf{x}$ and its components, such as translation $\mathbf{t}$, velocity $\mathbf{v}$ and rotation $\mathbf{R}$, are denoted as $(\cdot)^\text{LIO}$, $(\cdot)^\text{NN}$, and $(\cdot)^\text{NEW}$ for the LIO state, the neural network state, and the newly updated state, respectively. (a) Degeneracy ratio-based weighted update, (b) Projection onto the degenerate space, (c) Our ESKF-based update. Note that, in contrast to (a) and (b), our approach performs a full state update by leveraging the system uncertainty $\mathbf{P}^\text{LIO}$ and the data-driven uncertainty $\bm{\Sigma}^\text{NN}$, rather than updating only the velocity.
• Figure 2: Pipeline of ALIVE-LIO.
• Figure 3: Comparison of motion updates. (a) Box plot of the LiDAR-only translational change in well- and ill-conditioned (degenerate) scenarios. (b) Body-frame velocity in the degenerate scenario.
• Figure 4: Distribution of gravity ${}^W\mathbf{g}$, acceleration bias $\mathbf{b}_a$ and gyro bias $\mathbf{b}_g$ estimated by the LIO system. The $z$-component of gravity is shown after removing the well-known constant magnitude.
• Figure 5: Vehicle motion: Estimated trajectory visualization under degenerate conditions (X–Y plane in meters). The symbols $\triangle$ and ☆ denote the start and end points, respectively, of each trajectory.