In view of the lack of real-time performance in most of the current object-level semantic map construction systems. Combined with high real-time instance segmentation network and visual synchronous location and map construction (VSLAM) algorithm, a object-oriented semantic map real-time construction system is proposed. firstly, the system uses the instance segmentation network to segment each color image, obtains the object and combines the feature and spatial information provided by VSLAM to form the instance description information, and then matches the instance through feature consistency and spatial consistency. At the same time, Bayes filter is used to calculate the existence state of instances and spatial constraints are used to filter the error detection of instances. finally, a real-time instance-level semantic map construction system based on VSLAM is realized, and the system is tested with TUM data sets. The results show that the system can build object-level semantic maps in real time and reduce the error detection rate of instance segmentation.
KEYWORDS: Robots, Clouds, Image segmentation, Target recognition, Object recognition, 3D modeling, Robotics, Detection and tracking algorithms, Cameras, RGB color model
Aiming at the problem that service robots have poor dexterity in grasping objects with arbitrary postures in the home environment, a dexterous grasping method adapted to objects with arbitrary postures is proposed. First, the YOLACT instance segmentation network is used to recognize and segment the target object, and the segmented target object is registered with the depth image to obtain the target point cloud. Then the target point cloud and the template in the template library are used to estimate the accurate pose of the target object by using the ICP algorithm. Finally, according to the obtained object pose, the grasping pose of the robotic arm is standardized to achieve dexterous grasping of the object. The experimental test shows that the grasping method proposed in this paper has a high total success rate of grasping objects in different postures and the grasping method is beneficial to improve the dexterity of home service robots for grasping objects, and is significant for the development of home service robots.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.