D$^2$-LIO: Enhanced Optimization for LiDAR-IMU Odometry Considering Directional Degeneracy

TL;DR

This paper presents an improved LiDAR-inertial odometry framework that adaptively handles feature degeneracy and integrates IMU data to enhance robustness and accuracy in complex environments.

Contribution

It introduces an adaptive outlier removal threshold and a flexible scan-to-submap registration method that leverages IMU data, addressing feature degeneracy issues in LIO.

Findings

Outperforms state-of-the-art methods in robustness and accuracy

Effective in environments with sparse or degenerate features

Improves pose estimation in complex scenarios

Abstract

LiDAR-inertial odometry (LIO) plays a vital role in achieving accurate localization and mapping, especially in complex environments. However, the presence of LiDAR feature degeneracy poses a major challenge to reliable state estimation. To overcome this issue, we propose an enhanced LIO framework that integrates adaptive outlier-tolerant correspondence with a scan-to-submap registration strategy. The core contribution lies in an adaptive outlier removal threshold, which dynamically adjusts based on point-to-sensor distance and the motion amplitude of platform. This mechanism improves the robustness of feature matching in varying conditions. Moreover, we introduce a flexible scan-to-submap registration method that leverages IMU data to refine pose estimation, particularly in degenerate geometric configurations. To further enhance localization accuracy, we design a novel weighting matrix that fuses IMU preintegration covariance with a degeneration metric derived from the scan-to-submap process. Extensive experiments conducted in both indoor and outdoor environments-characterized by sparse or degenerate features-demonstrate that our method consistently outperforms state-of-the-art approaches in terms of both robustness and accuracy.