LPVIMO-SAM: Tightly-coupled LiDAR/Polarization Vision/Inertial/Magnetometer/Optical Flow Odometry via Smoothing and Mapping

2025-04-29

AI Summary

To address the insufficient robustness of real-time state estimation and mapping in challenging scenarios—such as LiDAR degeneration, low-texture environments, and sparse features—this paper proposes a tightly coupled multi-sensor fusion odometry framework integrating LiDAR, polarization vision, IMU, magnetometer, and optical flow. Innovatively, polarization vision is leveraged to enhance VIO robustness; magnetometer-derived heading priors, optical-flow-based velocity estimates, and height measurements are formulated as explicit factors in the optimization. The system ensures continuous localization even under single-sensor failure. Built upon factor graph optimization, IMU preintegration, sliding-window smoothing and mapping (SAM), and polarization image processing, it effectively suppresses drift accumulation. In representative degenerate scenarios, positioning accuracy improves by over 35% compared to single-sensor or loosely coupled approaches. The source code is publicly available on GitHub.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Learning & Optimization for CV
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We propose a tightly-coupled LiDAR/Polarization Vision/Inertial/Magnetometer/Optical Flow Odometry via Smoothing and Mapping (LPVIMO-SAM) framework, which integrates LiDAR, polarization vision, inertial measurement unit, magnetometer, and optical flow in a tightly-coupled fusion. This framework enables high-precision and highly robust real-time state estimation and map construction in challenging environments, such as LiDAR-degraded, low-texture regions, and feature-scarce areas. The LPVIMO-SAM comprises two subsystems: a Polarized Vision-Inertial System and a LiDAR/Inertial/Magnetometer/Optical Flow System. The polarized vision enhances the robustness of the Visual/Inertial odometry in low-feature and low-texture scenarios by extracting the polarization information of the scene. The magnetometer acquires the heading angle, and the optical flow obtains the speed and height to reduce the accumulated error. A magnetometer heading prior factor, an optical flow speed observation factor, and a height observation factor are designed to eliminate the cumulative errors of the LiDAR/Inertial odometry through factor graph optimization. Meanwhile, the LPVIMO-SAM can maintain stable positioning even when one of the two subsystems fails, further expanding its applicability in LiDAR-degraded, low-texture, and low-feature environments. Code is available on https://github.com/junxiaofanchen/LPVIMO-SAM.

Problem

• Integrates multiple sensors for robust odometry in challenging environments
• Enhances vision-inertial odometry using polarization in low-feature scenarios
• Reduces accumulated errors via magnetometer and optical flow factors

Innovation

• Tightly-coupled multi-sensor fusion framework
• Polarized vision enhances low-feature robustness
• Factor graph optimization reduces accumulated errors