PL-VIWO: A Lightweight and Robust Point-Line Monocular Visual Inertial Wheel Odometry

2025-03-01

AI Summary

To address the poor robustness and sparse feature availability in long-term visual-inertial localization for ground robots operating in dynamic, low-texture outdoor environments, this paper proposes a lightweight tightly coupled monocular Visual-Inertial-Wheel Odometry (VIWO) system. Methodologically, it introduces the first joint 2D point-and-line geometric constraint modeling to enhance feature matching and triangulation stability; incorporates a Motion Consistency Check (MCC) mechanism to dynamically reject dynamic or outlier features; and fuses point-line features, IMU preintegration, and wheel-encoder motion priors. Extensive experiments on public benchmarks demonstrate that the proposed method outperforms state-of-the-art approaches in localization accuracy, robustness, and real-time performance—particularly under challenging low-texture and dynamic interference conditions. The source code is publicly available.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents a novel tightly coupled Filter-based monocular visual-inertial-wheel odometry (VIWO) system for ground robots, designed to deliver accurate and robust localization in long-term complex outdoor navigation scenarios. As an external sensor, the camera enhances localization performance by introducing visual constraints. However, obtaining a sufficient number of effective visual features is often challenging, particularly in dynamic or low-texture environments. To address this issue, we incorporate the line features for additional geometric constraints. Unlike traditional approaches that treat point and line features independently, our method exploits the geometric relationships between points and lines in 2D images, enabling fast and robust line matching and triangulation. Additionally, we introduce Motion Consistency Check (MCC) to filter out potential dynamic points, ensuring the effectiveness of point feature updates. The proposed system was evaluated on publicly available datasets and benchmarked against state-of-the-art methods. Experimental results demonstrate superior performance in terms of accuracy, robustness, and efficiency. The source code is publicly available at: https://github.com/Happy-ZZX/PL-VIWO

Problem

• Develops a monocular visual-inertial-wheel odometry system for ground robots.
• Addresses challenges in obtaining visual features in dynamic or low-texture environments.
• Introduces line features and Motion Consistency Check for improved localization accuracy.

Innovation

• Integrates line features for geometric constraints
• Uses Motion Consistency Check for dynamic points
• Combines point-line features for robust matching