Natalnet Associate Laboratories, Federal University of Rio Grande do Norte, Campus Universitário, Natal RN 59.078-970, Brazil.
Institute of Computing, Fluminense Federal University, Campus Praia Vermelha, Niteroi RJ 24.310-346, Brazil.
Sensors (Basel). 2018 Sep 16;18(9):3122. doi: 10.3390/s18093122.
We propose a versatile method for estimating the RMS error of depth data provided by generic 3D sensors with the capability of generating RGB and depth () data of the scene, i.e., the ones based on techniques such as structured light, time of flight and stereo. A common checkerboard is used, the corners are detected and two point clouds are created, one with the real coordinates of the pattern corners and one with the corner coordinates given by the device. After a registration of these two clouds, the RMS error is computed. Then, using curve fittings methods, an equation is obtained that generalizes the RMS error as a function of the distance between the sensor and the checkerboard pattern. The depth errors estimated by our method are compared to those estimated by state-of-the-art approaches, validating its accuracy and utility. This method can be used to rapidly estimate the quality of RGB-D sensors, facilitating robotics applications as SLAM and object recognition.
我们提出了一种通用的方法，用于估计具有生成场景的 RGB 和深度（）数据能力的通用 3D 传感器提供的深度数据的均方根误差，即基于结构光、飞行时间和立体视觉等技术的传感器。使用常见的棋盘格，检测角点并创建两个点云，一个具有图案角点的真实坐标，另一个具有设备给出的角点坐标。在这两个云的注册之后，计算 RMS 误差。然后，使用曲线拟合方法，得到一个将 RMS 误差作为传感器和棋盘格图案之间的距离的函数的方程。通过将我们的方法估计的深度误差与最先进的方法估计的深度误差进行比较，验证了其准确性和实用性。该方法可用于快速估计 RGB-D 传感器的质量，促进机器人应用，如 SLAM 和目标识别。
