Enhancing Inverse Perspective Mapping for Automatic Vectorized Road Map Generation

2026-01-27

AI Summary

This work addresses the limitations of traditional inverse perspective mapping (IPM), which relies on the coplanarity assumption and struggles to accurately recover the 3D positions of lane markings and road symbols. To overcome this constraint, the authors propose an enhanced unified IPM framework that models lane markings as Catmull-Rom splines and other road symbols as polygons, leveraging instance segmentation outputs to refine the 3D coordinates of control points. The framework jointly optimizes the IPM homography matrix and vehicle pose, enabling the generation of centimeter-accurate vectorized road maps without requiring expensive sensors. The resulting IPM transformation achieves calibration-level accuracy comparable to manual annotation and significantly improves vehicle pose estimation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Visual Reasoning & Symbolic Representations
• Planning, Routing, and Scheduling: Planning with Language Models

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

In this study, we present a low-cost and unified framework for vectorized road mapping leveraging enhanced inverse perspective mapping (IPM). In this framework, Catmull-Rom splines are utilized to characterize lane lines, and all the other ground markings are depicted using polygons uniformly. The results from instance segmentation serve as references to refine the three-dimensional position of spline control points and polygon corner points. In conjunction with this process, the homography matrix of IPM and vehicle poses are optimized simultaneously. Our proposed framework significantly reduces the mapping errors associated with IPM. It also improves the accuracy of the initial IPM homography matrix and the predicted vehicle poses. Furthermore, it addresses the limitations imposed by the coplanarity assumption in IPM. These enhancements enable IPM to be effectively applied to vectorized road mapping, which serves a cost-effective solution with enhanced accuracy. In addition, our framework generalizes road map elements to include all common ground markings and lane lines. The proposed framework is evaluated in two different practical scenarios, and the test results show that our method can automatically generate high-precision maps with near-centimeter-level accuracy. Importantly, the optimized IPM matrix achieves an accuracy comparable to that of manual calibration, while the accuracy of vehicle poses is also significantly improved.

Problem

• Inverse Perspective Mapping
• Vectorized Road Map
• Homography Matrix
• Vehicle Pose
• Coplanarity Assumption

Innovation

• Inverse Perspective Mapping
• Vectorized Road Mapping
• Catmull-Rom Splines
• Homography Optimization
• Instance Segmentation