Improving the perception of visual fiducial markers in the field using Adaptive Active Exposure Control
원문 초록
Accurate localization is fundamental for autonomous underwater vehicles (AUVs) to carry out precise tasks, such as manipulation and construction. Vision-based solutions using fiducial marker are promising, but extremely challenging underwater because of harsh lighting condition underwater. This paper introduces a gradient-based active camera exposure control method to tackle sharp lighting variations during image acquisition, which can establish better foundation for subsequent image enhancement procedures. Considering a typical scenario for underwater operations where visual tags are used, we proposed several experiments comparing our method with other state-of-the-art exposure control method including Active Exposure Control (AEC) and Gradient-based Exposure Control (GEC). Results show a significant improvement in the accuracy of robot localization. This method is an important component that can be used in visual-based state estimation pipeline to improve the overall localization accuracy.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
Learning to See in the Extremely Dark
Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
RT-X Net: RGB-Thermal cross attention network for Low-Light Image Enhancement
토론
이 논문과 연결된 포럼 글이 아직 없습니다.