Monocular SLAM Supported Object Recognition
| Autor: | Sudeep Pillai, John J. Leonard |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
0209 industrial biotechnology
Monocular business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cognitive neuroscience of visual object recognition 02 engineering and technology Object (computer science) 020901 industrial engineering & automation Encoding (memory) Scalability 0202 electrical engineering electronic engineering information engineering Key (cryptography) Feature (machine learning) RGB color model 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | Robotics: Science and Systems |
| DOI: | 10.15607/rss.2015.xi.034 |
| Popis: | In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis. The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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