Pose estimation that locates objects in a bin is necessary for a robotic bin picking system. Although many algorithms have shown high performance in pose estimation, most algorithms estimate the poses of objects regardless of their occlusion. This can reduce the success rate in picking up the object. To resolve this issue, we propose a novel pipeline that estimates a pose only for occlusion-free objects based on point pair feature-based pose estimation with multiple edge appearance model (PPF-MEAM). The proposed method detects occlusion-free objects in the 2D image captured by a camera with a convolutional neural network framework. Next, corresponding point clouds of occlusion-free objects need to be extracted by using their locations in the 2D image. we propose a robust extraction method that finds the 3D points corresponding to image pixels in the 2D image to reduce the effect of the calibration errors between the camera and 3D sensor. The point cloud of the occlusion-free objects is finally input into a pipeline of PPF-MEAM to estimate the pose of the object. The experiment results prove that the proposed method is about 50% faster 30% higher in terms of pose estimation success rate compared with the original PPF. Moreover, it increases the success rate of picking tasks compared with the original PPF-MEAM.

Wave-dissipating blocks are the armor elements of breakwaters that protect beaches, ports, and harbors from erosion by waves. Monitoring the poses of individual wave-dissipating blocks benefits the accuracy of the block supplemental work plan, recording of the construction status, and monitoring of long-term pose change in blocks. This study proposes a deep-learning-based approach to detect individual blocks from large-scale three-dimensional point clouds measured with a pile of wave-dissipating blocks placed overseas and underseas using UAV photogrammetry and a multibeam echo-sounder. The approach comprises three main steps. First, the instance segmentation using our originally designed deep convolutional neural network partitions an original point cloud into small subsets of points, each corresponding to an individual block. Then, the block-wise 6D pose is estimated using a three-dimensional feature descriptor, point cloud registration, and CAD models of blocks. Finally, the type of each segmented block is identified using model registration results. The results of the instance segmentation on real-world and synthetic point cloud data achieved 70–90% precision and 50–76% recall with an intersection of union threshold of 0.5. The pose estimation results on synthetic data achieved 83–95% precision and 77–95% recall under strict pose criteria. The average block-wise displacement error was 30 mm, and the rotation error was less than $2^{\circ }$. The pose estimation results on real-world data showed that the fitting error between the reconstructed scene and the scene point cloud ranged between 30 and 50 mm, which is below 2% of the detected block size. The accuracy in the block-type classification on real-world point clouds reached about 95%. These block detection performances demonstrate the effectiveness of our approach.