In this work, we estimate the depth in which domestic waste are located in space from a mobile robot in outdoor scenarios. As we are doing this calculus on a broad range of space (0.3 - 6.0 m), we use RGB-D camera and LiDAR fusion. With this aim and range, we compare several methods such as average, nearest, median and center point, applied to those which are inside a reduced or non-reduced Bounding Box (BB). These BB are obtained from segmentation and detection methods which are representative of these techniques like Yolact, SOLO, You Only Look Once (YOLO)v5, YOLOv6 and YOLOv7. Results shown that, applying a detection method with the average technique and a reduction of BB of 40%, returns the same output as segmenting the object and applying the average method. Indeed, the detection method is faster and lighter in comparison with the segmentation one. The committed median error in the conducted experiments was 0.0298 ${\pm}$ 0.0544 m.

翻译：在这项工作中,我们估计了户外情景中移动机器人在空间中产生的国内废物的深度。当我们在广泛的空间(0.3-6.0米)上进行计算时,我们使用RGB-D相机和LIDAR聚变。为了这个目的和范围,我们比较了一些方法,例如平均、接近、中位和中点,这些方法适用于在缩小或未缩小的环球盒(BBB)内使用的方法。这些BB来自分解和探测方法,这些方法代表了这些技术,例如Yolact、SOLO、You only Look (YOLO)v5、YOLOv6和YOLOv7。结果显示,采用平均技术的探测方法,将BB减少40%,返回与分割物体和适用平均方法相同的输出。事实上,探测方法比分解法更快、更轻。在进行实验时发生的中位错误为0.0298美元0.044米。