School of Information Engineering, East China Jiaotong University, Nanchang 330013, China.
Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA.
Sensors (Basel). 2018 Nov 13;18(11):3908. doi: 10.3390/s18113908.
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.
RGB-D 相机提供了周围环境的彩色和深度图像，因此成为机器人和视觉应用的理想选择。 本工作介绍了 BRISK_D 算法，该算法有效地结合了加速段测试（FAST）和二进制鲁棒不变可扩展关键点（BRISK）方法的特征。 在 BRISK_D 算法中，通过 FAST 算法检测关键点，并在尺度和空间中细化关键点的位置。 关键点的比例因子直接使用图像的深度信息计算。 在实验中，我们从尺度、旋转、透视和模糊等方面对 SURF、BRISK 和 BRISK_D 这三种算法进行了详细的对比分析。BRISK_D 算法结合了深度信息，具有良好的算法性能。
