In robotic cockpit inspection scenarios, the 6D pose of highly-variable panel objects is necessary. However, the buttons with different states on the panel cause the variable texture and point cloud, which confuses the traditional invariable object pose estimation method. The bottleneck is the variable texture and point cloud. To address this issue, we propose a simple yet effective method denoted as PanelPose that leverages synthetic data and edge-line features. Specifically, we extract edge and line features of RGB images and fuse these feature maps as a multi-feature fusion map (MFF Map) to focus on the shape features of panel objects. Moreover, we design an effective keypoint selection algorithm considering the shape information of panel objects, which simplifies keypoint localization for precise pose estimation. Finally, the panel object pose is estimated via PNP/RANSAC, refined by the multi-state template (MST) and multi-scale ICP. We experimentally show that state-of-the-art 6D pose estimation methods alone are not sufficient to solve the cockpit panel inspection task but that our method significantly improves the performance. In cockpit inspection scenarios, the panel localization error is less than 3mm using our method. Code and data are available at https://github.com/sunhan1997/PaneIPose.

Grasping for novel objects is important for robot manipulation in unstructured environments. Most of current works require a grasp sampling process to obtain grasp candidates, combined with local feature extractor using deep learning. This pipeline is time-costly, expecially when grasp points are sparse such as at the edge of a bowl.In this paper, we propose an end-to-end approach to directly predict the poses, categories and scores (qualities) of all the grasps. It takes the whole sparse point clouds as the input and requires no sampling or search process. Moreover, to generate training data of multi-object scene, we propose a fast multi-object grasp detection algorithm based on Ferrari Canny metrics. A single-object dataset (79 objects from YCB object set, 23.7k grasps) and a multi-object dataset (20k point clouds with annotations and masks) are generated. A PointNet++ based network combined with multi-mask loss is introduced to deal with different training points. The whole weight size of our network is only about 11.6M, which takes about 102ms for a whole prediction process using a GeForce 840M GPU. Our experiment shows our work get 71.43% success rate and 91.60% completion rate, which performs better than current state-of-art works.