.png){kind=icon}
返回
Visual-Marker-Based Localization for Flat-Variation Scene
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2024.3372231.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Localization, an indispensable component in robotics and automation, encounters difficulties arising from appearance variations, resulting in inaccurate data associations. Semantic-based positioning mitigates these challenges by filtering out invalid data, such as moving vehicles and worn road markings. Building on this insight, we introduce a robust semantic visual localization system, which has been successfully deployed in real-world settings. The system uses neural networks to extract road markers and associate data with a semantic map. To enhance system reliability, we use several data filters. These filters remove images that are prone to misrecognition or poorly processed by neural networks. We propose two techniques for vehicle state estimation. The first, using the inverse perspective mapping (IPM) matrix, directly determines the vehicle's central pose. The second technique derives the camera pose using the perspective-n-points (PnP) method and leverages external parameters to infer the vehicle's central state. The wheel encoder, with its robust anti-noise capability, offers odometry in the absence of semantic information, enhancing the system's resilience. The crux of our approach lies in distinct semantic strategies: using lane lines for orientation and road markers exclusively for translation estimation. We also detail an automatic construction method for the semantic map, enhancing the system's practicality. Experimental results indicate that the IPM method outperforms the PnP approach, leading to notably improved positioning accuracy. In addition, the error distribution of the IPM method more closely aligns with a normal distribution compared with that of the PnP approach.
Keyword:
Inverse perspective mapping (IPM)
normal distribution
perspective-n-points (PnP)
semantic visual localization
期刊
IF:
5.9
论文数:
1.9W
被引数:
5.8W
机构
引用论文
Label-Free Protein-RNA Interactome Analysis Identifies Khsrp Signaling Downstream of the p38/Mk2 Kinase Complex as a Critical Modulator of Cell Cycle Progression.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容