Why You Cannot Rank First: Modifications for Benchmarking Six-Degree-of-Freedom Visual Localization Algorithms
文献类型：期刊论文
| 作者 | Han, Sheng1,2; Gao, Wei1,2 ; Hu, Zhanyi1,2
|
| 刊名 | SENSORS
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 23期号:23页码:18 |
| 关键词 | visual localization benchmark enhancement pose compensation sequential interpolation ties resolution |
| DOI | 10.3390/s23239580 |
| 通讯作者 | Gao, Wei(wgao@nlpr.ia.ac.cn) |
| 英文摘要 | Robust and precise visual localization over extended periods of time poses a formidable challenge in the current domain of spatial vision. The primary difficulty lies in effectively addressing significant variations in appearance caused by seasonal changes (summer, winter, spring, autumn) and diverse lighting conditions (dawn, day, sunset, night). With the rapid development of related technologies, more and more relevant datasets have emerged, which has also promoted the progress of 6-DOF visual localization in both directions of autonomous vehicles and handheld devices.This manuscript endeavors to rectify the existing limitations of the current public benchmark for long-term visual localization, especially in the part on the autonomous vehicle challenge. Taking into account that autonomous vehicle datasets are primarily captured by multi-camera rigs with fixed extrinsic camera calibration and consist of serialized image sequences, we present several proposed modifications designed to enhance the rationality and comprehensiveness of the evaluation algorithm. We advocate for standardized preprocessing procedures to minimize the possibility of human intervention influencing evaluation results. These procedures involve aligning the positions of multiple cameras on the vehicle with a predetermined canonical reference system, replacing the individual camera positions with uniform vehicle poses, and incorporating sequence information to compensate for any failed localized poses. These steps are crucial in ensuring a just and accurate evaluation of algorithmic performance. Lastly, we introduce a novel indicator to resolve potential ties in the Schulze ranking among submitted methods. The inadequacies highlighted in this study are substantiated through simulations and actual experiments, which unequivocally demonstrate the necessity and effectiveness of our proposed amendments. |
| WOS关键词 | PLACE RECOGNITION |
| 资助项目 | National Key R&D Program of China |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001116610500001 |
| 资助机构 | National Key R&D Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55171]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Gao, Wei |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Han, Sheng,Gao, Wei,Hu, Zhanyi. Why You Cannot Rank First: Modifications for Benchmarking Six-Degree-of-Freedom Visual Localization Algorithms[J]. SENSORS,2023,23(23):18. |
| APA | Han, Sheng,Gao, Wei,&Hu, Zhanyi.(2023).Why You Cannot Rank First: Modifications for Benchmarking Six-Degree-of-Freedom Visual Localization Algorithms.SENSORS,23(23),18. |
| MLA | Han, Sheng,et al."Why You Cannot Rank First: Modifications for Benchmarking Six-Degree-of-Freedom Visual Localization Algorithms".SENSORS 23.23(2023):18. |
入库方式： OAI收割
来源：自动化研究所
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