Enhancing Inverse Perspective Mapping for Automatic Vectorized Road Map Generation
Hongji Liu, Linwei Zheng, Yongjian Li, Mingkai Tang, Xiaoyang Yan, Ming Liu, Jun Ma

TL;DR
This paper introduces an improved, cost-effective framework for vectorized road map generation using enhanced inverse perspective mapping, which refines lane and ground markings with high accuracy and reduces mapping errors.
Contribution
It presents a novel unified framework that enhances IPM accuracy, refines vehicle poses, and generalizes road markings for automatic high-precision map generation.
Findings
Achieves near-centimeter-level accuracy in map generation.
Significantly reduces IPM mapping errors.
Improves vehicle pose estimation accuracy.
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…
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Taxonomy
TopicsAutomated Road and Building Extraction · Autonomous Vehicle Technology and Safety · Robotics and Sensor-Based Localization
