基于传感器融合的户外环境中生活垃圾检测与深度估计
Detection and depth estimation for domestic waste in outdoor environments by sensors fusion
AI总结：
本研究融合RGB-D相机与LiDAR，在0.3-6.0米范围内估计户外生活垃圾深度，比较多种方法后发现检测结合平均值及40%边界框缩小可媲美分割效果，且更快更轻，中位误差为0.0298±0.0544米。
摘要：
在本工作中，我们估计了户外场景中移动机器人周围空间中生活垃圾所处的深度。由于我们在一个广泛的空间范围（0.3 - 6.0 米）内进行此计算，我们使用了 RGB-D 相机与 LiDAR 的融合。针对这一目标和范围，我们比较了多种方法，如平均值、最近邻、中位数和中心点，并将其应用于那些位于缩小或未缩小的边界框（BB）内的点。这些边界框是通过分割和检测方法获得的，这些方法代表了这些技术，如 Yolact、SOLO、You Only Look Once (YOLO)v5、YOLOv6 和 YOLOv7。结果表明，采用检测方法结合平均值技术并缩小边界框 40%，返回的输出与对物体进行分割并应用平均值方法的结果相同。实际上，检测方法相比分割方法更快且更轻量。在进行的实验中，所提交的中位误差为 0.0298 ± 0.0544 米。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
