Abstract
RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Keypoints (BRISK) methods. In the BRISK_D algorithm, the keypoints are detected by the FAST algorithm and the location of the keypoint is refined in the scale and the space. The scale factor of the keypoint is directly computed with the depth information of the image. In the experiment, we have made a detailed comparative analysis of the three algorithms SURF, BRISK and BRISK_D from the aspects of scaling, rotation, perspective and blur. The BRISK_D algorithm combines depth information and has good algorithm performance.
Highlights
In the field of machine vision and robotics research, the feature detection has attracted the attention of scholars at home and abroad
In order to build a descriptor with rotation invariance and scale invariance, sampling pattern rotates θ angle around the feature point k. θ is computed by: θ = actan2( gy, gx )
From the brief analysis above, it can be seen that the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm realizes the scale invariance of descriptors by detecting feature points in multi-scale layer and realizes the rotational invariance of descriptors by determining direction of master mode using long-distance pixel pairs
Summary
In the field of machine vision and robotics research, the feature detection has attracted the attention of scholars at home and abroad. The BRISK algorithm is a feature point detection and description algorithm with scale invariance and rotation invariance. Algorithms only based on texture information of 2D image are widely used, such as the SURF algorithm, SIFT algorithm, BRIEF algorithm and BRISK algorithm. The BRISK algorithm will be improved using the depth information of the RGB-D image and intensity centroid. The scale factor of the keypoint is directly computed with the depth information of the image. The experimental results show that, compared with the original BRISK algorithm, the improved BRISK algorithm’s robustness of rotation and scale invariance are stronger.
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