PL-VIWO2: A Lightweight, Fast and Robust Visual-Inertial-Wheel Odometry Using Points and Lines

2025-09-25

AI Summary

To address the degeneracy and insufficient robustness of visual odometry in complex urban environments, this paper proposes a lightweight tightly coupled visual-inertial-wheel filter-based pose estimation algorithm. Methodologically: (1) a joint point–line geometric association framework is constructed to efficiently exploit structured features; (2) an SE(2)-constrained SE(3) wheel-velocity preintegration model is introduced to improve motion modeling accuracy for wheeled platforms; (3) an IMU- and wheel-velocity-driven motion consistency verification mechanism is designed to effectively reject dynamic features. The system supports monocular or stereo input and integrates fast line-feature triangulation with dynamic feature filtering. Extensive experiments on Monte Carlo simulations and public autonomous driving datasets demonstrate that the proposed method achieves superior accuracy, real-time performance, and long-term robustness compared to current state-of-the-art approaches.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• 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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Vision-based odometry has been widely adopted in autonomous driving owing to its low cost and lightweight setup; however, its performance often degrades in complex outdoor urban environments. To address these challenges, we propose PL-VIWO2, a filter-based visual-inertial-wheel odometry system that integrates an IMU, wheel encoder, and camera (supporting both monocular and stereo) for long-term robust state estimation. The main contributions are: (i) a novel line feature processing framework that exploits the geometric relationship between 2D feature points and lines, enabling fast and robust line tracking and triangulation while ensuring real-time performance; (ii) an SE(2)-constrained SE(3) wheel pre-integration method that leverages the planar motion characteristics of ground vehicles for accurate wheel updates; and (iii) an efficient motion consistency check (MCC) that filters out dynamic features by jointly using IMU and wheel measurements. Extensive experiments on Monte Carlo simulations and public autonomous driving datasets demonstrate that PL-VIWO2 outperforms state-of-the-art methods in terms of accuracy, efficiency, and robustness.

Problem

• Enhances odometry accuracy in complex outdoor urban environments
• Integrates IMU, wheel encoder and camera for robust state estimation
• Addresses performance degradation using points and lines feature tracking

Innovation

• Integrates IMU, wheel encoder, and camera for robust odometry
• Uses geometric relationship between points and lines for tracking
• Employs SE(2)-constrained SE(3) wheel pre-integration for accuracy