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

2026-04-03

AI Summary

This work addresses the significant performance degradation of LiDAR-inertial odometry in degenerate environments—such as long corridors or scenes with a single wall—where insufficient geometric features lead to poor pose estimation. To mitigate this issue, the authors propose ALIVE-LIO, a novel framework that uniquely integrates deep learning with an error-state Kalman filter (ESKF). Specifically, a degeneracy-aware mechanism dynamically triggers a neural network to predict body-frame velocity, which is then selectively fused into the ESKF to compensate for the loss of observability in degenerate directions. This approach enhances localization robustness under challenging conditions while preserving filter consistency. Experimental results demonstrate that ALIVE-LIO substantially reduces pose drift across multiple public and self-collected degenerate datasets, achieving state-of-the-art accuracy in 22 out of 32 tested sequences.

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.

Problem

• LiDAR-inertial odometry
• degenerate environments
• observability loss
• pose drift
• ESKF

Innovation

• degeneracy-aware
• neural velocity prediction
• ESKF
• LiDAR-inertial odometry
• observability compensation