PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry

2025-06-23

AI Summary

To address the degeneracy and poor robustness of LiDAR-inertial odometry (LIO) in geometrically degenerate environments—such as textureless, self-similar, or weakly structured scenes—this paper proposes a tightly coupled LIO framework that jointly exploits photometric and geometric information. It introduces, for the first time, LiDAR photometric consistency constraints into the LIO formulation, integrating them with geometric feature matching and IMU preintegration factors within a sliding-window nonlinear optimization framework. This tight fusion significantly enhances pose estimation stability under challenging conditions and effectively mitigates system degeneracy. Extensive evaluations on both public and in-house datasets demonstrate state-of-the-art performance: for instance, the method achieves only ~1 m drift over a 1-km trajectory in a tunnel—a substantial improvement over leading approaches.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

LiDAR-Inertial Odometry (LIO) is widely used for accurate state estimation and mapping which is an essential requirement for autonomous robots. Conventional LIO methods typically rely on formulating constraints from the geometric structure sampled by the LiDAR. Hence, in the lack of geometric structure, these tend to become ill-conditioned (degenerate) and fail. Robustness of LIO to such conditions is a necessity for its broader deployment. To address this, we propose PG-LIO, a real-time LIO method that fuses photometric and geometric information sampled by the LiDAR along with inertial constraints from an Inertial Measurement Unit (IMU). This multi-modal information is integrated into a factor graph optimized over a sliding window for real-time operation. We evaluate PG-LIO on multiple datasets that include both geometrically well-conditioned as well as self-similar scenarios. Our method achieves accuracy on par with state-of-the-art LIO in geometrically well-structured settings while significantly improving accuracy in degenerate cases including against methods that also fuse intensity. Notably, we demonstrate only 1 m drift over a 1 km manually piloted aerial trajectory through a geometrically self-similar tunnel at an average speed of 7.5m/s (max speed 10.8 m/s). For the benefit of the community, we shall also release our source code https://github.com/ntnu-arl/mimosa

Problem

• Enhance LiDAR-Inertial Odometry robustness in geometric-degenerate environments
• Fuse photometric and geometric data for accurate state estimation
• Address LIO failure in self-similar or structure-less scenarios

Innovation

• Fuses photometric and geometric LiDAR data
• Integrates multi-modal data into factor graph
• Optimizes over sliding window for real-time