A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
Source abstract
Abstract
Visual-based positioning technology plays a pivotal role in spatial artificial intelligence, facilitating precise perception and comprehension of the physical world for robotic platforms and augmented reality devices. In this study, we propose a binocular camera-based method for spatial localization of targets using CNN for instance segmentation while simultaneously providing target location information. The method encompasses image acquisition and correction, target recognition and segmentation, and stereo matching, among other components. Building upon this foundation, we introduce a pedestrian recognition segmentation network model with an attention mechanism. To accurately locate the target, we employ a multi-feature fusion feature point extraction and matching algorithm that combines edge information with semantic information. Finally, our proposed method is evaluated for dynamic pedestrian targets in indoor environments, achieving a horizontal positioning error of less than 0.25 m.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Remote Sensing and LiDAR Applications · 3D Surveying and Cultural Heritage
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Instrumental Science and Engineering, Southeast University, Nanjing 210037, China
State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China
The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
Funding
Key Research Development Program of State · Hebei Province Technology Research and Development platform construction
References (26)
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