研究目的
To make OFS's sensitivity robust to surface texture variations by designing a novel Iterative Point Tracking Algorithm (IPTA).
研究成果
IPTA effectively enhances the robustness of OFS' resolution to surface texture variations, reducing the standard deviation of resolutions by 76.7%. The algorithm's iterative structure for outlier removal is efficient, though further improvements are possible.
研究不足
The algorithm's performance is affected by noise in brightness's value and directional characteristics of surface's textures' reflectance. There is still room for improvement in identifying incorrect matching between feature points.
1：Experimental Design and Method Selection:
The study proposes a novel architecture combining OFS with IPTA to enhance robustness to surface texture variations.
2：Sample Selection and Data Sources:
Four different surfaces' textures (iron, paper, textile, granite stone) are used.
3：List of Experimental Equipment and Materials:
Includes ADNS-3080 OFS, ADNS-2120 lens, STM32F407 discovery board, and a dual axis ball screw drive system.
4：Experimental Procedures and Operational Workflow:
The sensor is fixed, and the surface below is moved. IPTA is performed on images captured from the surfaces.
5：Data Analysis Methods:
The performance of IPTA is compared with the on-chip algorithm of the sensor. Optimal algorithm parameters are calculated using GA.
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