Search

To deal with the challenges of object detection and object grabbing tasks for service robots, we propose an object detection and segmentation algorithm based on the deep convolutional neural network (CNN) and depth-first algorithm to achieve object grabbing by using an RGB-D camera and the six-degree-of-freedom robotic manipulator. Firstly, the improved particle swarm optimization (PSO) algorithm is proposed to calibrate and optimize the hand-eye system of the experimental platform, which is a mobile robot equipped with UR5 mechanical arm and Kinect V2 sensor. Then, we collect the environmental information by the camera, where the depth images restored by the joint bilateral filtering algorithm and the original color images are calibrated. Finally, a depth learning method is used to detect the objects, and the depth information is used to achieve objects segmentation. We complete object grabbing based on the estimated 3D coordinates, which has proven the practicability and effectiveness of our proposed grabbing framework.

An omnidirectional mobile platform is designed for building point cloud based on an improved filtering algorithm which is employed to handle the depth image. First, the mobile platform can move flexibly and the control interface is convenient to control. Then, because the traditional bilateral filtering algorithm is time-consuming and inefficient, a novel method is proposed which called local bilateral filtering (LBF). LBF is applied to process depth image obtained by the Kinect sensor. The results show that the effect of removing noise is improved comparing with the bilateral filtering. In the condition of off-line, the color images and processed images are used to build point clouds. Finally, experimental results demonstrate that our method improves the speed of processing time of depth image and the effect of point cloud which has been built.