Year: 2024
Type: article
Abstract: Accurate object registration is crucial for precise localization and environment sensing in autonomous driving systems. While real-time sensors such as cameras and radar capture the local environment, high-definition (HD) maps provide a global reference frame that enhances localization accuracy and robustness, especially in complex scenarios. In this paper, we propose an innovative method called enhanced object registration (EOR) to improve the accuracy and robustness of object registration between camera images and HD maps. Our research investigates the influence of spatial distribution factors and spatial structural characteristics of objects in visual perception and HD maps on registration accuracy and robustness. We specifically focus on understanding the varying importance of different object types and the constrained dimensions of pose estimation. These factors are integrated into a nonlinear optimization model and extended Kalman filter framework. Through comprehensive experimentation on the open-source Argoverse 2 dataset, the proposed EOR demonstrates the ability to maintain high registration accuracy in lateral and elevation dimensions, improve longitudinal accuracy, and increase the probability of successful registration. These findings contribute to a deeper understanding of the relationship between sensing data and scenario understanding in object registration for vehicle localization.
Source: Remote Sensing
DOI: 10.3390/rs17010066
Authors: Nian Hui, Zijie Jiang, Zhongliang Cai, Shen Ying
Institution: Wuhan University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,069
Award: G2107206239
OpenAlex ID: W4405967291