https://scholars.lib.ntu.edu.tw/handle/123456789/635499
標題: | Image Preprocessing with Enhanced Feature Matching for Map Merging in the Presence of Sensing Error | 作者: | Chen, Yu Lin KUEI-YUAN CHAN |
關鍵字: | image stitching | map merge | occupancy grid map | simultaneous localization and mapping | 公開日期: | 1-八月-2023 | 卷: | 23 | 期: | 16 | 來源出版物: | Sensors | 摘要: | Autonomous robots heavily rely on simultaneous localization and mapping (SLAM) techniques and sensor data to create accurate maps of their surroundings. When multiple robots are employed to expedite exploration, the resulting maps often have varying coordinates and scales. To achieve a comprehensive global view, the utilization of map merging techniques becomes necessary. Previous studies have typically depended on extracting image features from maps to establish connections. However, it is important to note that maps of the same location can exhibit inconsistencies due to sensing errors. Additionally, robot-generated maps are commonly represented in an occupancy grid format, which limits the availability of features for extraction and matching. Therefore, feature extraction and matching play crucial roles in map merging, particularly when dealing with uncertain sensing data. In this study, we introduce a novel method that addresses image noise resulting from sensing errors and applies additional corrections before performing feature extraction. This approach allows for the collection of features from corresponding locations in different maps, facilitating the establishment of connections between different coordinate systems and enabling effective map merging. Evaluation results demonstrate the significant reduction of sensing errors during the image stitching process, thanks to the proposed image pre-processing technique. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/635499 | ISSN: | 14248220 | DOI: | 10.3390/s23167303 |
顯示於: | 機械工程學系 |
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