Point-Line LIVO Using Patch-Based Gradient Optimization for Degenerate Scenes

Abstract

Simultaneous localization and mapping based on 3-D light detection and ranging (LiDAR) tends to degenerate in structural-less environments, leading to a distinct reduction in localization accuracy and mapping precision. This article proposes a point-line LiDAR-visual-inertial odometry (PL-LIVO) based on the system implementation of FAST-LIVO for robust localization in LiDAR-degenerate scenes. The key idea is integrating both points and lines into the proposed direct visual odometry subsystem (PL-DVO). By minimizing the patch-based gradient residuals for state optimization, PL-DVO provides additional constraints complementary to LiDAR. Furthermore, a LiDAR map assisted visual features depth extraction (LM-VDE) method is proposed to recover 3-D positions of visual features by mapping them onto the 3-D planes of the LiDAR map. This method is independent of the single scan's density and notable for superior generalization across various LiDAR sensors. Extensive experiments on both public datasets and our datasets demonstrate that PL-LIVO ensures robust localization and outperforms other state-of-the-art systems in LiDAR degenerate scenes.