AC-LIO: Towards Asymptotic Compensation for Distortion in LiDAR-Inertial Odometry via Selective Intra-Frame Smoothing

TL;DR

AC-LIO introduces a novel LiDAR-Inertial Odometry framework that asymptotically compensates for motion distortion within LiDAR frames, significantly improving long-term localization accuracy with minimal additional computation.

Contribution

The paper presents AC-LIO, a new LIO framework that uses selective intra-frame smoothing and asymptotic backpropagation to better correct motion distortions, enhancing odometry accuracy.

Findings

Achieves 30.4% reduction in average RMSE compared to prior methods.

Improves long-term and large-scale localization accuracy.

Demonstrates minimal computational overhead.

Abstract

Existing LiDAR-Inertial Odometry (LIO) methods typically utilize the prior trajectory derived from the IMU integration to compensate for the motion distortion within LiDAR frames. However, discrepancies between the prior and true trajectory can lead to residual motion distortions that compromise the consistency of LiDAR frame with its corresponding geometric environment. This imbalance may result in pointcloud registration becoming trapped in local optima, thereby exacerbating drift during long-term and large-scale localization. To this end, we propose a novel LIO framework with selective intra-frame smoothing dubbed AC-LIO. Our core idea is to asymptotically backpropagate current update term and compensate for residual motion distortion under the guidance of convergence criteria, aiming to improve the accuracy of discrete-state LIO system with minimal computational increase. Extensive experiments demonstrate that our AC-LIO framework further enhances odometry accuracy compared to prior arts, with about 30.4% reduction in average RMSE over the second best result, leading to marked improvements in the accuracy of long-term and large-scale localization and mapping.